人工智慧在製药领域的应用：按组件、技术、治疗领域、应用、部署类型和最终用户划分－2025-2032年全球预测

预计到 2032 年，人工智慧 (AI) 在製药领域的市场规模将达到 1,111.3 亿美元，复合年增长率为 27.61%。

策略实施：概述人工智慧如何重塑药物研发、临床营运、监管路径、生产流程和商业策略。

人工智慧不再只是製药营运中的实验性辅助工具；它已发展成为一项至关重要的战略能力，影响着药物发现科学、临床开发、监管策略、生产营运和商业性决策。本文将人工智慧定位为一种系统级力量，而非仅仅是一系列技术的集合，它正在重塑药物生命週期中知识的创造方式、决策自动化的实现方式以及风险管理的管理方式。因此，相关人员必须从多个观点看待人工智慧：将其视为药物发现中假设生成的催化剂、用于精准识别患者和优化临床试验的工具、用于监管合规的分析引擎以及用于提升供应链韧性的营运赋能者。

为了成功驾驭这种环境，领导者必须理解三个交织的动态因素。首先，运算能力、资料基础架构和模型架构的进步正在拓展可解决的问题范围。其次，特定领域平台和检验工作流程的成熟正在减少调查团队和临床团队之间的整合摩擦。第三，法规和伦理期望随着技术能力的提升而不断发展，这使得可重复性、可解释性和稳健检验的重要性日益凸显。因此，製药业对人工智慧的应用越来越受到以结果为导向的实施的驱动，这些实施强调週期时间、品质和以患者为中心的可衡量改进，而不是技术本身。

这项实施分析为更深入的思考奠定了基础，重点阐述了其对研发总监、临床营运总监、法规负责人、生产总监和商业主管的实际意义。它强调了跨职能管治、清晰的技术和资料整合蓝图以及平衡平台开发与有针对性的概念验证(PoC)倡议的投资策略的必要性。简而言之，随着人工智慧从新兴技术走向营运基础，那些能够将自身技术能力与临床和法规目标相契合的组织将从中获益匪浅。

透过人工智慧，对正在重新定义药物发现、临床实验设计、监管参与、生产製造和病人参与的变革性技术和组织转变进行深入、全面的分析。

製药业的格局正在经历一场变革性的转变，其驱动力包括技术突破、组织思维模式的转变以及外部政策的影响。在技​​术层面，模型架构、训练方法和特定领域演算法的进步正在拓展自动化和预测的边界。卷积类神经网路、生成对抗网路、循环神经网路和变压器等深度学习创新，结合监督学习、无监督学习和强化学习等实用机器学习技术，使得解决复杂的生物医学问题成为可能。同时，包括影像分割、医学影像应用和目标检测在内的电脑视觉技术，正在为诊断和临床前测试分析开闢新的途径。此外，自然语言处理技术，例如情绪分析、语音辨识和文字探勘，能够从医疗记录、监管文件和文献中提取可操作的洞见。

在组织层面，我们看到一个明显的转变，即从孤立的概念验证转向整合软体和服务产品的大规模部署。组件层面的细分錶明，从临床试验管理平台和诊断工具到药物发现平台、法规遵循工具和供应链管理解决方案等软体领域，都得到了包含託管服务和专业服务在内的服务生态系统的补充。这种服务和软体的整合，透过将技术实施与领域专业知识结合，加快了价值实现的速度。同时，临床试验、药物发现、个人化医疗和供应链优化等应用领域也正日趋成熟。临床试验自动化正在扩展到患者招募、临床数据管理、预测分析和基于风险的监测，而药物发现工作流程则正在整合电脑辅助先导化合物优化、标靶选择和最终模型检验。

这些变化也正在推动人才和伙伴关係策略的重塑。生命科学公司正在投资组建跨学科团队，这些团队汇集了生物医学研究人员、资料工程师和法规专家。受託研究机构(CRO) 和技术供应商正越来越多地与製药公司合作，共同开发检验的工作流程，并确保其可重复性。这些技术和组织变革的结合，正在创造一个全新的竞争格局，在这个格局中，速度、严谨性和法规检验是关键的差异化因素。

重点分析2025年美国关税对人工智慧驱动的医药供应链、跨境采购、生产投入和合作模式的累积影响

2025年的关税格局为人工智慧驱动的製药业务的采购、供应链规划和跨境合作增添了更多复杂性。影响硬体进口、试剂采购、临床设备和软体许可的关税可能会对整个生态系统产生连锁反应。例如，提高专用运算硬体和实验室设备的关税可能会增加本地部署的总拥有成本，从而使将运算风险外包的云端基础解决方案更具经济优势。相反，针对某些软体即服务 (SaaS) 模式或捆绑解决方案的关税可能会使采购重点转向模组化架构和在地化服务模式。

除了直接的成本影响外，关税还会影响供应商的选择和筹资策略。为了降低贸易政策变化带来的风险，企业会采取多种应对措施，例如供应商多元化、加快对替代供应商的资格认证以及重新评估其区域製造地。这些调整通常会影响检验和监管申报的时间表，因为引入新供应商和不同设备会增加额外的文件负担。此外，由于企业希望减少对关键零件和生物製药生产投入品的跨境依赖，关税也可能影响企业在近岸外包和回流生产方面的投资决策。

此外，关税也将影响跨境合作研究和资料共用安排。海关检查力度加大以及进口法规的变更可能会使合作试验所需的生物样本、专用试剂和设备的运输变得更加复杂。跨国专案可能需要赞助公司重新设计物流路线、重新评估第三方供应商合同，并更新风险登记册以反映关税相关的延误。为此，具有前瞻性的机构正在将供应链透明度、多源合格和合约灵活性作为其业务永续营运计划的优先事项。虽然关税不会改变人工智慧应用的科学可行性，但它们会对这些应用的部署和规模化营运路径产生重大影响。

综合細項分析，解释组件、技术系列、治疗领域、应用领域、实施模型和最终用户概况如何相互关联，从而确定人工智慧在製药业的应用和价值创造。

要了解人工智慧在製药业创造价值的途径和方式，需要对影响其应用模式和结果的多个细分维度进行统一的视角分析。基于组件的格局由服务和软体构成。服务又可细分为託管服务和专业服务，而软体则包括临床试验管理软体、诊断软体、药物发现平台、法规遵循工具和供应链管理软体。这种组件层面的观点揭示了许多将软体平台与上线和营运管理支援相结合的实际应用案例，旨在确保符合监管要求并保障业务连续性。

目录

第一章：序言

第二章调查方法

第三章执行摘要

第四章 市场概览

第五章 市场洞察

• 将生成式人工智慧应用于加速候选药物的结构优化和合成规划
• 应用联邦学习框架实现安全的多机构药物资料交换
• 引入人工智慧驱动的数位双胞胎模型，用于临床试验中的个人化药物动力学和动态模拟
• 开发可解释的人工智慧演算法，以确保复杂药物核准流程中的监管合规性
• 利用深度学习模型对靶向蛋白质-蛋白质相互作用的生物製药进行高通量In Silico筛检
• 利用人工智慧引导的机器人平台开发分析自动化高内涵细胞检测方法
• 利用社群媒体和电子健康记录（EHR）资料流，实施即时人工智慧药物监测系统

第六章：美国关税的累积影响，2025年

第七章：人工智慧的累积影响，2025年

第八章：按成分分類的药品市场

• 服务
  • 託管服务
  • 专业服务
• 软体
  • 临床试验管理软体
  • 诊断软体
  • 药物发现平台
  • 监理合规工具
  • 供应链管理软体

第九章：依科技分類的医药市场

• 电脑视觉
  • 影像分割
  • 医学影像
  • 目标侦测
• 深度学习
  • 卷积类神经网路
  • 生成对抗网络
  • 循环神经网络
  • 变压器
• 机器学习
  • 强化学习
  • 监督式学习
  • 无监督学习
• 自然语言处理
  • 情绪分析
  • 语音辨识
  • 文字探勘
• 机器人流程自动化

第十章：依治疗领域分類的药品市场

• 循环系统疾病
• 免疫学
• 感染疾病
• 代谢性疾病
• 神经病学
• 肿瘤学
• 呼吸系统疾病

第十一章 按应用分類的医药市场

• 临床试验
  • 临床数据管理
  • 病患招募
  • 预测分析
  • 基于风险的监测
• 药物发现
  • 药物发现设计
  • 结束模型检验
  • 先导药物最适化
  • 目标选择
• 个人化医疗
  • 生物标记发现
  • 基因组分析
  • 精准医疗发展
• 供应链管理
  • 需求预测
  • 库存管理
  • 物流最佳化

第十二章 依部署类型分類的药品市场

• 云端基础的
• 本地部署

第十三章：依最终用户分類的药品市场

• 学术和研究机构
• 合约研究组织（CRO）
• 製药和生物技术公司

第十四章：各地区的医药市场

• 美洲
  • 北美洲
  • 拉丁美洲
• 欧洲、中东和非洲
  • 欧洲
  • 中东
  • 非洲
• 亚太地区

第十五章：依组别分類的药品市场

• ASEAN
• GCC
• EU
• BRICS
• G7
• NATO

第十六章：各国药品市场

• 美国
• 加拿大
• 墨西哥
• 巴西
• 英国
• 德国
• 法国
• 俄罗斯
• 义大利
• 西班牙
• 中国
• 印度
• 日本
• 澳洲
• 韩国

第十七章 竞争格局

• 2024年市占率分析
• FPNV定位矩阵，2024
• 竞争分析
  • AiCure, LLC
  • Aspen Technology Inc.
  • Atomwise Inc.
  • BenevolentAI SA
  • BioSymetrics Inc.
  • BPGbio Inc.
  • Butterfly Network, Inc.
  • Cloud Pharmaceuticals, Inc.
  • Cyclica by Recursion Pharmaceuticals, Inc.
  • Deargen Inc.
  • Deep Genomics Incorporated
  • Deloitte Touche Tohmatsu Limited
  • Euretos Services BV
  • Exscientia PLC
  • Insilico Medicine
  • Intel Corporation
  • International Business Machines Corporation
  • InveniAI LLC
  • Isomorphic Labs Limited
  • Microsoft Corporation
  • Novo Nordisk A/S
  • NVIDIA Corporation
  • Oracle Corporation
  • SANOFI WINTHROP INDUSTRIE
  • Turbine Ltd.
  • Viseven Europe OU
  • XtalPi Inc.

The Artificial Intelligence in Pharmaceutical Market is projected to grow by USD 111.13 billion at a CAGR of 27.61% by 2032.

A strategic introduction outlining how artificial intelligence is reshaping pharmaceutical research, clinical operations, regulatory pathways, manufacturing workflows, and commercial strategies

Artificial intelligence is no longer an experimental adjunct in pharmaceutical workstreams; it has become an integral strategic capability that touches discovery science, clinical development, regulatory strategy, manufacturing operations, and commercial decision-making. This introduction frames AI not merely as a set of technologies but as a system-level force reshaping how knowledge is generated, decisions are automated, and risks are managed across the lifecycle of medicines. Stakeholders must therefore view AI through multiple lenses: as an accelerant for hypothesis generation in drug discovery, as a precision tool for patient identification and trial optimization, as an analytics engine for regulatory compliance, and as an operational enabler for supply chain resilience.

To navigate this environment, leaders must appreciate three converging dynamics. First, advances in compute, data infrastructure, and model architectures are broadening the range of tractable problems. Second, the maturation of domain-specific platforms and validated workflows is lowering integration friction for research and clinical teams. Third, regulatory and ethical expectations are co-evolving with capabilities, increasing the importance of reproducibility, explainability, and robust validation. As a result, AI adoption in pharmaceuticals is increasingly driven by outcome-oriented deployments that emphasize measurable improvements in cycle time, quality, and patient-centricity rather than technology for its own sake.

This introductory analysis sets the stage for deeper examination by emphasizing practical implications for R&D leaders, clinical operations directors, regulatory strategists, manufacturing heads, and commercial executives. It establishes the imperative for cross-functional governance, a clear technology- and data-integration roadmap, and an investment posture that balances platform development with targeted proof-of-concept initiatives. In short, organizations that align technical capability with clinical and regulatory objectives are positioned to capture disproportionate value as AI transitions from novelty to operational backbone.

A detailed synthesis of transformative technological and organizational shifts that are redefining drug discovery, trial design, regulatory interactions, manufacturing, and patient engagement through AI

The pharmaceutical landscape is undergoing transformative shifts driven by technological breakthroughs, shifting organizational mindsets, and external policy influences. At the technology level, advances in model architectures, training regimes, and domain-adapted algorithms are expanding the frontier of what can be automated and predicted. Deep learning innovations in convolutional neural networks, generative adversarial networks, recurrent neural networks, and transformers are increasingly coupled with pragmatic machine learning approaches such as supervised and unsupervised learning plus reinforcement learning to address complex biomedical problems. In parallel, computer vision capabilities including image segmentation, medical imaging applications, and object detection are unlocking new modalities for diagnostics and preclinical assay analysis, while natural language processing is enabling extraction of actionable insights from clinical notes, regulatory submissions, and literature through techniques such as sentiment analysis, speech recognition, and text mining.

Organizationally, there is a clear shift from isolated proofs of concept to scaled deployments that integrate software and service offerings. Component-level segmentation illustrates that software domains-ranging from clinical trial management platforms and diagnostic tools to drug discovery platforms, regulatory compliance tools, and supply chain management solutions-are being complemented by services ecosystems that include managed and professional services. This integration of services and software is accelerating time-to-value by combining technical implementation with domain expertise. Simultaneously, application domains such as clinical trials, drug discovery, personalized healthcare, and supply chain optimization are maturing; clinical trial automation is extending into patient recruitment, clinical data management, predictive analytics, and risk-based monitoring, while drug discovery workflows are embracing computational drug design, lead optimization, target selection, and end-model validation.

These shifts are also reshaping talent and partnership strategies. Life sciences organizations are investing in cross-disciplinary teams that combine biomedical scientists, data engineers, and regulatory specialists. Contract research organizations and technology vendors are forming deeper alliances with pharmaceutical companies to co-develop validated workflows and to ensure reproducibility. Taken together, these technical and organizational transformations are creating a new competitive dynamic where speed, rigor, and regulatory-aligned validation are the primary differentiators.

A focused analysis of the cumulative effects of United States tariff measures enacted in 2025 on AI-enabled pharmaceutical supply chains, cross-border sourcing, manufacturing inputs, and collaboration models

The tariff landscape introduced in 2025 has introduced additional complexity into procurement, supply chain planning, and cross-border collaboration for AI-enabled pharmaceutical operations. Tariff measures that affect hardware imports, reagent sourcing, clinical instrumentation, and software licensing can create ripple effects across the ecosystem. For example, increases in duties on specialized computing hardware or laboratory instrumentation raise the total cost of ownership for on-premises deployments and may tilt the economics in favor of cloud-based solutions where compute risks can be externalized. Conversely, tariffs that target certain software-as-a-service models or bundled solutions can shift procurement preferences toward modular architectures and localized service models.

Beyond direct cost impacts, tariffs affect supplier selection and sourcing strategies. Organizations respond by diversifying supplier bases, accelerating qualification of alternative vendors, and re-evaluating regional manufacturing footprints to mitigate exposure to trade policy shifts. These adjustments often influence timelines for validation and regulatory filings, because change controls associated with new suppliers or different equipment can introduce additional documentation burdens. In addition, tariffs can influence investment decisions in nearshoring or reshoring initiatives, where companies seek to reduce cross-border dependencies for critical components or biologics manufacturing inputs.

Moreover, tariffs have implications for collaborative research and data-sharing arrangements across borders. Increased customs scrutiny and shifting import regimes can complicate the transport of biological samples, specialized reagents, and equipment essential for collaborative trials. For multinational programs, sponsors may need to redesign logistics corridors, re-assess third-party provider contracts, and update risk registers to reflect tariff-induced delays. In response, savvy organizations are prioritizing supply chain visibility, multi-source qualification, and contractual flexibility as part of their operational resilience programs. While tariffs do not alter the scientific feasibility of AI applications, they meaningfully affect the operational pathways through which those applications are deployed and scaled.

Integrated segmentation insights explaining how components, technology families, therapeutic focus areas, application domains, deployment models, and end-user profiles jointly determine AI uptake and value creation in pharma

Understanding where and how AI generates value in pharmaceuticals requires an integrated view of multiple segmentation axes that together shape adoption patterns and outcomes. Based on Component, the landscape comprises Services and Software where Services splits into Managed Services and Professional Services and Software includes clinical trial management software, diagnostic software, drug discovery platforms, regulatory compliance tools, and supply chain management software. This component-level view clarifies that practical deployments frequently combine software platforms with implementation and managed support to ensure regulatory-grade performance and operational continuity.

Based on Technology, adopters must evaluate capabilities across computer vision, deep learning, machine learning, natural language processing, and robotic process automation; within these families there are important sub-specializations such as image segmentation, medical imaging, and object detection for computer vision, convolutional neural networks, generative adversarial networks, recurrent neural networks, and transformers for deep learning, and reinforcement learning, supervised learning, and unsupervised learning for machine learning, alongside sentiment analysis, speech recognition, and text mining for NLP. The multiplicity of approaches underscores the need for a technology taxonomy that maps each method to specific use cases and validation requirements.

Based on Therapeutic Area, AI initiatives often align with clinical priority and data maturity across cardiovascular diseases, immunology, infectious diseases, metabolic diseases, neurology, oncology, and respiratory diseases. Disease biology, endpoint definability, and data availability vary across these areas, which in turn affects algorithmic approachability and regulatory scrutiny. Based on Applications, deployment domains include clinical trials, drug discovery, personalized healthcare, and supply chain management with clinical trials subdividing into clinical data management, patient recruitment, predictive analytics, and risk-based monitoring, while drug discovery encompasses drug design, end-model validation, lead optimization, and target selection and personalized healthcare covers biomarker discovery, genomic profiling, and precision medicine development and supply chain management focuses on demand forecasting, inventory management, and logistics optimization.

Based on Deployment Type, choices between cloud-based and on-premises architectures have implications for data governance, latency, and cost structure, and based on End User, the primary consumers of these solutions span academic and research institutions, contract research organizations, and pharmaceutical and biotechnology companies. The intersections among these segmentation axes create contextual trade-offs: for example, oncology discovery efforts may preferentially adopt deep learning generative models and on-premises deployments when patient-level privacy and validation are paramount, while supply chain optimization workstreams commonly leverage cloud-based machine learning and managed services to maximize elasticity and cross-site visibility. Therefore, segmentation-aware strategies are essential to align technical design, validation planning, procurement strategy, and organizational capability development.

Comprehensive regional insights comparing strategic drivers, regulatory landscapes, investment trends, infrastructure readiness, and commercial adoption patterns across Americas, Europe Middle East & Africa, and Asia-Pacific

Regional dynamics exert a strong influence on how AI is adopted and scaled across the pharmaceutical value chain, with distinctive patterns emerging across the Americas, Europe, Middle East & Africa, and Asia-Pacific. In the Americas, a robust private sector investment environment, advanced cloud infrastructure, and established venture ecosystems have accelerated platform development and commercial deployments, while regulatory guidance in certain jurisdictions has moved toward outcomes-based validation and clearer frameworks for software as a medical device. This creates a favorable environment for companies that combine rapid iteration with strong evidence-generation capabilities.

In Europe, Middle East & Africa, regulatory rigor and data protection regimes shape design and deployment choices, often increasing the emphasis on explainability, localized data residency, and formalized validation paths. National policy initiatives and pan-European collaborations have also fostered consortium-based models for data sharing that enable multicenter trials and federated learning approaches. Meanwhile in parts of the Middle East and Africa, infrastructural variability and nascent data ecosystems require bespoke implementation models and capacity-building partnerships.

Asia-Pacific presents a heterogeneous but highly dynamic set of conditions where strong manufacturing clusters, rapidly growing clinical trial activity, and sizable patient populations create compelling use cases for AI. Several markets in the region are advancing digital health policies and public-private partnerships that accelerate deployment of diagnostic and clinical decision-support tools. Importantly, regional supply chain integration, proximity to key hardware suppliers, and an expanding talent base make Asia-Pacific an attractive locus for both development and scaled implementation projects. Across all regions, local regulatory expectations, talent availability, data governance frameworks, and infrastructure maturity determine the optimal mix of cloud versus on-premises deployment and the most effective partnership models for vendors and sponsors alike.

Company insights emphasizing competitive positioning, partnership strategies, product and service portfolios, platform differentiation, and go-to-market approaches among leading AI players supporting pharmaceutical innovation

Company behavior in the AI-for-pharma ecosystem demonstrates distinct strategic archetypes, including platform providers that invest in end-to-end product suites, specialized algorithm developers focusing on narrow high-value use cases, systems integrators that bridge domain expertise with scalable implementation, and contract research organizations that embed AI capabilities into outsourced development services. Leading organizations are differentiating through validated data assets, regulatory-compliant workflows, and capabilities that reduce integration friction for life sciences customers.

Across supplier strategies, we observe three persistent patterns. The first is platform consolidation where vendors expand horizontally to offer clinical trial, discovery, and compliance modules that interoperate within a single architecture. The second is vertical specialization where niche players concentrate on a therapeutic or modality-specific problem-such as imaging in oncology or genomic profiling in personalized medicine-and achieve deep validation within that domain. The third pattern is partnership ecosystems where companies join forces to combine proprietary algorithms, clinical data, and laboratory automation in order to deliver regulated outcomes.

From a procurement perspective, pharmaceutical and biotechnology customers increasingly evaluate vendors on evidence of real-world performance, regulatory readiness, and post-deployment support capabilities, rather than on feature checklists alone. As a result, successful companies prioritize clinical validation studies, transparent model governance, and comprehensive professional or managed services to ensure sustained operational performance. Contracts reflect these expectations with outcomes-linked milestones, change-control provisions, and clear responsibilities for data stewardship and model maintenance.

Actionable strategic recommendations for executives to accelerate responsible AI adoption by prioritizing governance, talent development, ecosystem partnerships, platform selection, and regulatory alignment across functions

Industry leaders seeking to accelerate responsible and strategic AI adoption should pursue a coherent mix of governance, talent, technology, and partnership actions. Begin by establishing cross-functional governance that assigns clear accountability for model development, validation, deployment, and monitoring; governance structures should integrate legal, regulatory, clinical, and technical stakeholders and define standardized validation protocols and audit trails to satisfy regulators and internal risk functions. Simultaneously, invest in talent programs that blend domain expertise with data science skills; rotational programs, embedded data scientists within therapeutic teams, and strategic hiring of regulatory-savvy machine learning engineers will shorten feedback loops and improve the alignment of algorithms with clinical objectives.

On the technology front, prioritize modular architectures that balance the benefits of cloud-based scalability with the control afforded by on-premises deployments where privacy or latency constraints demand it. Adopt open and transparent model governance practices, including versioning, reproducibility tests, and clear explainability artifacts tied to clinical endpoints. In parallel, develop an ecosystem strategy that differentiates between capabilities to build internally and those best accessed through partnerships with academic centers, CROs, or specialized vendors. Structured collaborations with contract research organizations can accelerate trial execution, while alliances with diagnostic firms and lab automation providers can de-risk end-to-end implementation.

Finally, align procurement and contracting approaches with performance-based outcomes and continuous validation requirements. Include provisions for post-deployment monitoring, change management, and retraining cycles in vendor agreements. Taken together, these steps provide a pragmatic roadmap for leaders to scale AI responsibly while delivering measurable clinical and operational improvements.

A transparent research methodology describing data sources, stakeholder interviews, technology taxonomy alignment, therapeutic mapping, validation processes, and analytical frameworks used to generate conclusions

The conclusions and insights in this report are grounded in a multi-method research approach combining primary and secondary evidence, expert interviews, and a technology- and therapeutic-focused taxonomy to ensure applicability across decision contexts. Data collection included structured discussions with interdisciplinary stakeholders across pharmaceutical companies, biotechnology firms, contract research organizations, clinical investigators, regulatory specialists, and technology vendors to validate practical constraints, preferred validation strategies, and deployment models. Secondary inputs comprised peer-reviewed literature, regulatory guidance documents, standards for software as a medical device, and public technical disclosures that inform model architectures and validation practices.

Analytically, the work uses a taxonomy that maps component types, technology families, therapeutic priorities, application domains, deployment models, and end-user segments to observed adoption patterns and implementation risks. Validation exercises included cross-referencing interview findings with documented case studies and technology white papers, and applying scenario analysis to explore the operational consequences of supply chain disruptions and policy changes. Quality assurance measures involved iterative peer review, triangulation of evidence across sources, and explicit documentation of assumptions and limitations. This methodology ensures that recommendations are traceable to observable practices and that the analytical framework remains adaptable to evolving regulatory and technical developments.

A concise conclusion synthesizing implications for R&D, clinical operations, regulatory strategy, commercial deployment, and supply chain resilience while emphasizing governance, skills, and collaboration imperatives

The cumulative analysis underscores a singular strategic reality: artificial intelligence is now a foundational capability for pharmaceutical organizations that seek to improve R&D productivity, enhance clinical trial efficiency, strengthen regulatory compliance, and optimize supply chain resilience. Success in this era depends not on chasing every technical novelty but on disciplined alignment of AI investments with clinical and regulatory priorities, rigorous validation practices, and robust operational governance. Organizations that combine domain-focused model development with partnerships that supply complementary data, lab automation, and implementation expertise will move faster from prototype to production.

Moreover, the interplay between policy levers-such as tariffs and data governance regimes-and operational execution highlights the need for continuous risk assessment and adaptive sourcing strategies. Effective deployment requires a pragmatic mix of cloud and on-premises approaches informed by privacy constraints and latency considerations, and contracting models that emphasize outcomes and post-deployment stewardship. Ultimately, building sustained advantage with AI in pharmaceuticals is a multi-year endeavor that hinges on reproducibility, explainability, and the capacity to learn from real-world performance. Executives who prioritize these elements will be positioned to convert technical capability into measurable clinical and business results.

Table of Contents

1. Preface

• 1.1. Objectives of the Study
• 1.2. Market Segmentation & Coverage
• 1.3. Years Considered for the Study
• 1.4. Currency & Pricing
• 1.5. Language
• 1.6. Stakeholders

2. Research Methodology

3. Executive Summary

4. Market Overview

5. Market Insights

• 5.1. Integration of generative AI for accelerated drug candidate structure optimization and synthesis planning
• 5.2. Application of federated learning frameworks for secure multi-center pharmaceutical data collaboration
• 5.3. Deployment of AI-driven digital twin models for personalized pharmacokinetic and dynamic simulations in trials
• 5.4. Development of explainable AI algorithms to ensure regulatory compliance in complex drug approval workflows
• 5.5. Adoption of deep learning models for high-throughput in silico screening of biologics targeting protein-protein interactions
• 5.6. Utilization of AI-guided robotic platforms for automated high-content cell-based assay development and analysis
• 5.7. Implementation of real-time AI-enabled pharmacovigilance systems leveraging social media and EHR data streams

6. Cumulative Impact of United States Tariffs 2025

7. Cumulative Impact of Artificial Intelligence 2025

8. Artificial Intelligence in Pharmaceutical Market, by Component

• 8.1. Services
  • 8.1.1. Managed Services
  • 8.1.2. Professional Services
• 8.2. Software
  • 8.2.1. Clinical Trial Management Software
  • 8.2.2. Diagnostic Software
  • 8.2.3. Drug Discovery Platforms
  • 8.2.4. Regulatory Compliance Tools
  • 8.2.5. Supply Chain Management Software

9. Artificial Intelligence in Pharmaceutical Market, by Technology

• 9.1. Computer Vision
  • 9.1.1. Image Segmentation
  • 9.1.2. Medical Imaging
  • 9.1.3. Object Detection
• 9.2. Deep Learning
  • 9.2.1. Convolutional Neural Networks
  • 9.2.2. Generative Adversarial Networks
  • 9.2.3. Recurrent Neural Networks
  • 9.2.4. Transformers
• 9.3. Machine Learning
  • 9.3.1. Reinforcement Learning
  • 9.3.2. Supervised Learning
  • 9.3.3. Unsupervised Learning
• 9.4. Natural Language Processing
  • 9.4.1. Sentiment Analysis
  • 9.4.2. Speech Recognition
  • 9.4.3. Text Mining
• 9.5. Robotic Process Automation

10. Artificial Intelligence in Pharmaceutical Market, by Therapeutic Area

• 10.1. Cardiovascular Diseases
• 10.2. Immunology
• 10.3. Infectious Diseases
• 10.4. Metabolic Diseases
• 10.5. Neurology
• 10.6. Oncology
• 10.7. Respiratory Diseases

11. Artificial Intelligence in Pharmaceutical Market, by Applications

• 11.1. Clinical Trials
  • 11.1.1. Clinical Data Management
  • 11.1.2. Patient Recruitment
  • 11.1.3. Predictive Analytics
  • 11.1.4. Risk-Based Monitoring
• 11.2. Drug Discovery
  • 11.2.1. Drug Design
  • 11.2.2. End-Model Validation
  • 11.2.3. Lead Optimization
  • 11.2.4. Target Selection
• 11.3. Personalized Healthcare
  • 11.3.1. Biomarker Discovery
  • 11.3.2. Genomic Profiling
  • 11.3.3. Precision Medicine Development
• 11.4. Supply Chain Management
  • 11.4.1. Demand Forecasting
  • 11.4.2. Inventory Management
  • 11.4.3. Logistics Optimization

12. Artificial Intelligence in Pharmaceutical Market, by Deployment Type

• 12.1. Cloud-Based
• 12.2. On-Premises

13. Artificial Intelligence in Pharmaceutical Market, by End User

• 13.1. Academic and Research Institutions
• 13.2. Contract Research Organizations (CROs)
• 13.3. Pharmaceutical & Biotechnology Companies

14. Artificial Intelligence in Pharmaceutical Market, by Region

• 14.1. Americas
  • 14.1.1. North America
  • 14.1.2. Latin America
• 14.2. Europe, Middle East & Africa
  • 14.2.1. Europe
  • 14.2.2. Middle East
  • 14.2.3. Africa
• 14.3. Asia-Pacific

15. Artificial Intelligence in Pharmaceutical Market, by Group

• 15.1. ASEAN
• 15.2. GCC
• 15.3. European Union
• 15.4. BRICS
• 15.5. G7
• 15.6. NATO

16. Artificial Intelligence in Pharmaceutical Market, by Country

• 16.1. United States
• 16.2. Canada
• 16.3. Mexico
• 16.4. Brazil
• 16.5. United Kingdom
• 16.6. Germany
• 16.7. France
• 16.8. Russia
• 16.9. Italy
• 16.10. Spain
• 16.11. China
• 16.12. India
• 16.13. Japan
• 16.14. Australia
• 16.15. South Korea

17. Competitive Landscape

• 17.1. Market Share Analysis, 2024
• 17.2. FPNV Positioning Matrix, 2024
• 17.3. Competitive Analysis
  • 17.3.1. AiCure, LLC
  • 17.3.2. Aspen Technology Inc.
  • 17.3.3. Atomwise Inc.
  • 17.3.4. BenevolentAI SA
  • 17.3.5. BioSymetrics Inc.
  • 17.3.6. BPGbio Inc.
  • 17.3.7. Butterfly Network, Inc.
  • 17.3.8. Cloud Pharmaceuticals, Inc.
  • 17.3.9. Cyclica by Recursion Pharmaceuticals, Inc.
  • 17.3.10. Deargen Inc.
  • 17.3.11. Deep Genomics Incorporated
  • 17.3.12. Deloitte Touche Tohmatsu Limited
  • 17.3.13. Euretos Services BV
  • 17.3.14. Exscientia PLC
  • 17.3.15. Insilico Medicine
  • 17.3.16. Intel Corporation
  • 17.3.17. International Business Machines Corporation
  • 17.3.18. InveniAI LLC
  • 17.3.19. Isomorphic Labs Limited
  • 17.3.20. Microsoft Corporation
  • 17.3.21. Novo Nordisk A/S
  • 17.3.22. NVIDIA Corporation
  • 17.3.23. Oracle Corporation
  • 17.3.24. SANOFI WINTHROP INDUSTRIE
  • 17.3.25. Turbine Ltd.
  • 17.3.26. Viseven Europe OU
  • 17.3.27. XtalPi Inc.

LIST OF FIGURES

• FIGURE 1. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, 2018-2032 (USD MILLION)
• FIGURE 2. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPONENT, 2024 VS 2032 (%)
• FIGURE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPONENT, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TECHNOLOGY, 2024 VS 2032 (%)
• FIGURE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TECHNOLOGY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY THERAPEUTIC AREA, 2024 VS 2032 (%)
• FIGURE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY THERAPEUTIC AREA, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY APPLICATIONS, 2024 VS 2032 (%)
• FIGURE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY APPLICATIONS, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEPLOYMENT TYPE, 2024 VS 2032 (%)
• FIGURE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEPLOYMENT TYPE, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY END USER, 2024 VS 2032 (%)
• FIGURE 13. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY END USER, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 14. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 15. AMERICAS ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 16. NORTH AMERICA ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 17. LATIN AMERICA ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 18. EUROPE, MIDDLE EAST & AFRICA ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUBREGION, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 19. EUROPE ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 20. MIDDLE EAST ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 21. AFRICA ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 22. ASIA-PACIFIC ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 23. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GROUP, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 24. ASEAN ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 25. GCC ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 26. EUROPEAN UNION ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 27. BRICS ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 28. G7 ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 29. NATO ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 30. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COUNTRY, 2024 VS 2025 VS 2032 (USD MILLION)
• FIGURE 31. ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SHARE, BY KEY PLAYER, 2024
• FIGURE 32. ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET, FPNV POSITIONING MATRIX, 2024

LIST OF TABLES

• TABLE 1. ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SEGMENTATION & COVERAGE
• TABLE 2. UNITED STATES DOLLAR EXCHANGE RATE, 2018-2024
• TABLE 3. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, 2018-2024 (USD MILLION)
• TABLE 4. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, 2025-2032 (USD MILLION)
• TABLE 5. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPONENT, 2018-2024 (USD MILLION)
• TABLE 6. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPONENT, 2025-2032 (USD MILLION)
• TABLE 7. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, 2018-2024 (USD MILLION)
• TABLE 8. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, 2025-2032 (USD MILLION)
• TABLE 9. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 10. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 11. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 12. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 13. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 14. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SERVICES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 15. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 16. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 17. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 18. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 19. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 20. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MANAGED SERVICES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 21. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 22. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 23. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 24. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 25. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 26. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PROFESSIONAL SERVICES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 27. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, 2018-2024 (USD MILLION)
• TABLE 28. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, 2025-2032 (USD MILLION)
• TABLE 29. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 30. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 31. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 32. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 33. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 34. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SOFTWARE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 35. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 36. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 37. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 38. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 39. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 40. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIAL MANAGEMENT SOFTWARE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 41. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 42. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 43. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 44. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 45. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 46. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DIAGNOSTIC SOFTWARE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 47. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 48. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 49. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 50. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 51. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 52. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DRUG DISCOVERY PLATFORMS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 53. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 54. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 55. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 56. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 57. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 58. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REGULATORY COMPLIANCE TOOLS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 59. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY REGION, 2018-2024 (USD MILLION)
• TABLE 60. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY REGION, 2025-2032 (USD MILLION)
• TABLE 61. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 62. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 63. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 64. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPPLY CHAIN MANAGEMENT SOFTWARE, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 65. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TECHNOLOGY, 2018-2024 (USD MILLION)
• TABLE 66. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TECHNOLOGY, 2025-2032 (USD MILLION)
• TABLE 67. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, 2018-2024 (USD MILLION)
• TABLE 68. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, 2025-2032 (USD MILLION)
• TABLE 69. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 70. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 71. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 72. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 73. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 74. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY COMPUTER VISION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 75. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 76. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 77. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 78. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 79. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 80. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMAGE SEGMENTATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 81. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 82. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 83. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 84. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 85. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 86. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MEDICAL IMAGING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 87. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 88. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 89. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 90. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 91. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 92. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY OBJECT DETECTION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 93. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, 2018-2024 (USD MILLION)
• TABLE 94. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, 2025-2032 (USD MILLION)
• TABLE 95. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 96. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 97. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 98. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 99. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 100. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY DEEP LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 101. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 102. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 103. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 104. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 105. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 106. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CONVOLUTIONAL NEURAL NETWORKS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 107. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 108. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 109. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 110. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 111. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 112. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY GENERATIVE ADVERSARIAL NETWORKS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 113. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 114. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 115. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 116. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 117. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 118. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RECURRENT NEURAL NETWORKS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 119. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 120. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 121. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 122. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 123. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 124. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TRANSFORMERS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 125. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, 2018-2024 (USD MILLION)
• TABLE 126. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, 2025-2032 (USD MILLION)
• TABLE 127. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 128. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 129. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 130. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 131. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 132. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY MACHINE LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 133. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 134. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 135. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 136. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 137. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 138. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY REINFORCEMENT LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 139. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 140. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 141. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 142. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 143. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 144. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SUPERVISED LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 145. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 146. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 147. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 148. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 149. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 150. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY UNSUPERVISED LEARNING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 151. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2018-2024 (USD MILLION)
• TABLE 152. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, 2025-2032 (USD MILLION)
• TABLE 153. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 154. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 155. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 156. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 157. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 158. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NATURAL LANGUAGE PROCESSING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 159. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 160. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 161. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 162. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 163. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 164. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SENTIMENT ANALYSIS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 165. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 166. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 167. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 168. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 169. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 170. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY SPEECH RECOGNITION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 171. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY REGION, 2018-2024 (USD MILLION)
• TABLE 172. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY REGION, 2025-2032 (USD MILLION)
• TABLE 173. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 174. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 175. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 176. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY TEXT MINING, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 177. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY REGION, 2018-2024 (USD MILLION)
• TABLE 178. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY REGION, 2025-2032 (USD MILLION)
• TABLE 179. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 180. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 181. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 182. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ROBOTIC PROCESS AUTOMATION, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 183. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY THERAPEUTIC AREA, 2018-2024 (USD MILLION)
• TABLE 184. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY THERAPEUTIC AREA, 2025-2032 (USD MILLION)
• TABLE 185. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 186. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 187. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 188. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 189. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 190. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CARDIOVASCULAR DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 191. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY REGION, 2018-2024 (USD MILLION)
• TABLE 192. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY REGION, 2025-2032 (USD MILLION)
• TABLE 193. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 194. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 195. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 196. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY IMMUNOLOGY, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 197. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 198. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 199. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 200. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 201. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 202. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY INFECTIOUS DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 203. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 204. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 205. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 206. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 207. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 208. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY METABOLIC DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 209. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY REGION, 2018-2024 (USD MILLION)
• TABLE 210. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY REGION, 2025-2032 (USD MILLION)
• TABLE 211. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 212. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 213. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 214. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY NEUROLOGY, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 215. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY REGION, 2018-2024 (USD MILLION)
• TABLE 216. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY REGION, 2025-2032 (USD MILLION)
• TABLE 217. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 218. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 219. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 220. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY ONCOLOGY, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 221. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY REGION, 2018-2024 (USD MILLION)
• TABLE 222. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY REGION, 2025-2032 (USD MILLION)
• TABLE 223. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 224. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 225. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 226. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY RESPIRATORY DISEASES, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 227. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY APPLICATIONS, 2018-2024 (USD MILLION)
• TABLE 228. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY APPLICATIONS, 2025-2032 (USD MILLION)
• TABLE 229. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, 2018-2024 (USD MILLION)
• TABLE 230. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, 2025-2032 (USD MILLION)
• TABLE 231. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 232. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 233. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 234. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 235. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 236. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL TRIALS, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 237. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY REGION, 2018-2024 (USD MILLION)
• TABLE 238. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY REGION, 2025-2032 (USD MILLION)
• TABLE 239. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 240. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 241. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 242. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY CLINICAL DATA MANAGEMENT, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 243. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY REGION, 2018-2024 (USD MILLION)
• TABLE 244. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY REGION, 2025-2032 (USD MILLION)
• TABLE 245. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 246. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 247. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY COUNTRY, 2018-2024 (USD MILLION)
• TABLE 248. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PATIENT RECRUITMENT, BY COUNTRY, 2025-2032 (USD MILLION)
• TABLE 249. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY REGION, 2018-2024 (USD MILLION)
• TABLE 250. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY REGION, 2025-2032 (USD MILLION)
• TABLE 251. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY GROUP, 2018-2024 (USD MILLION)
• TABLE 252. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY GROUP, 2025-2032 (USD MILLION)
• TABLE 253. GLOBAL ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MARKET SIZE, BY PREDICTIVE ANALYTICS, BY COUNTRY, 2018-2024 (USD MILLION)

TABLE 254.