医疗保健产生人工智慧的成长机会

药物研发发现与开发、医学影像分析、数位双胞胎、合成资料、电子健康记录管理和虚拟助理等领域的新应用

生成式人工智慧在医疗保健中的应用是一个快速成长的领域。近年来，药物研发和开发、医学影像分析、数位双胞胎、合成资料、电子健康记录管理和虚拟助理等新应用引起了人们的广泛关注，因为它们有可能彻底改变这一领域。这为技术开发人员（包括软体公司和医疗保健行业的新兴企业）创造了一个充满希望的机会，将这些新兴技术置于其产品开发研发工作的中心。

本报告对新兴应用的审查范围是多方面的，包括技术、临床决策支援、安全性和经济等领域的考察。该研究的重点是确定整个医疗保健行业采用的传统护理方法之外的新差距领域。

顶级软体公司正在将自己定位为技术开发商，而生成式人工智慧新兴企业则专注于小而重要的方面，以减轻临床负担并提供高效的临床决策支援系统。由于这些公司及其合作，本研究深入探讨了潜在的好处和结果，突出了竞争行业和相应相关人员的前景，并探讨了未来五年药物、诊断和治疗计划的未来性效益和结果，揭示了生成式人工智慧的演变。

本报告回答的问题

• 1.医疗保健领域生成式人工智慧的成长动力和抑制因素是什么？
• 2.生成式人工智慧模型在药物研发和开发、医学影像分析、数位双胞胎、合成资料、电子健康记录管理和虚拟助理方面有哪些类型和优势？
• 3.生成AI医疗产业主要企业有何动作？
• 4.业界主要的合作伙伴和资金筹措有哪些？
• 5.主要的生成式人工智慧法规有哪些？

目录

战略问题

• 为什么成长如此困难？策略要务 8 (TM)：阻碍成长的因素
• 战略要务8（略）
• 关键策略激励措施对产生人工智慧产业的影响
• Growth Pipeline Engine（TM）：加速成长机会
• 调查方法

成长机会分析

• 生成式人工智慧的演进：过去与现在
• 生成式人工智慧透过多模式方法在医疗保健中发挥新作用
• 在医疗保健资料上实施多模式生成人工智慧
• 医疗保健产业的各个方面都采用了生成式人工智慧
• 生成式人工智慧在医疗保健中的应用：应用范围
• 生成式人工智慧在医疗保健中的应用：细分
• 生长促进因子
• 成长抑制因素

生成式人工智慧在医疗保健中的技术应用简介

• 用于药物研发发现和药物开发的生成式人工智慧
• 用于医学影像分析的生成式人工智慧
• 用于合成资料和数数位双胞胎创建的生成式人工智慧
• 用于 EHR 和虚拟助理管理的生成式 AI
• 公司对医疗保健领域生成式人工智慧的见解：简介
• 行业简介
• 医疗保健中的生成式人工智慧：比较技术采用和技术成熟度

创新生态系统

• 生成式人工智慧与云端运算基础设施共同开发
• 製药公司提供大量资金来开发基于人工智慧的药物研发平台
• 医院公司为医疗保健法学硕士的新兴市场提供资金
• 医疗保健世代人工智慧组织的地理分布
• 部分主要国家生成式人工智慧法规概况

充满成长机会的世界

• 成长机会1：轻量级云端即时实现生成式AI
• 成长机会 2：虚拟临床试验的综合资料创建
• 成长机会 3：医疗互通性

附录

• 技术成熟度等级 (TRL)：说明

下一步

Emerging Applications in Drug Discovery and Development, Medical Imaging Analysis, Digital Twins, Synthetic Data, Electronic Health Record Management, and Virtual Assistants

The application of Generative AI (Gen AI) in healthcare is a rapidly growing field. In recent years, emerging applications in drug discovery and development, medical imaging analysis, digital twins, synthetic data, and electronic health records management, and virtual assistants have garnered significant interest thanks to their potential to revolutionize the field. This creates a promising opportunity for technology developers-such as software companies and startups-in the healthcare industry to center their R&D activities on these emerging technologies for their product development.

This report's scope of analysis for emerging applications is multifaceted and involves considerations across technical, clinical decision support, safety, and economic domains. The primary focus of the study is to identify the emerging gap areas being adopted across the healthcare industry that will surpass conventional care methodologies.

With top-tier software companies positioning themselves as technology developers and Gen AI startups focusing on small but crucial aspects toward reducing the clinical burden and providing efficient clinical decision support systems and their collaborations, this study will delve into the potential benefits and outcomes, highlighting prospective and corresponding stakeholders in the competitive industry, and the evolution of generative AI across drugs, diagnostics, and treatment planning in the next five years.

Questions that this report answers:

• 1. What are the growth drivers and restraints of Gen AI in healthcare?

• 2. What are the types of models and benefits of Gen AI in drug discovery and development, medical imaging analysis, digital twins, synthetic data, electronic health record management, and virtual assistants?

• 3. What are the key companies to action in the Gen AI healthcare industry?

• 4. What are key collaborations and funding in the industry?

• 5. What are the key Gen AI regulations?

Table of Contents

Strategic Imperatives

• Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth
• The Strategic Imperative 8™
• The Impact of the Top 3 Strategic Imperatives on the Generative Artificial Intelligence Industry
• Growth Opportunities Fuel the Growth Pipeline Engine™
• Research Methodology

Growth Opportunity Analysis

• The Evolution of Gen AI: Then and Now
• Emerging Roles of Gen AI in Medicine to Incorporate the Multimodal Approach
• Implementing Multimodal Generative AI on Healthcare Data
• Various Facets of the Healthcare Industry That Employ Gen AI
• Applications of Gen AI in Healthcare: Scope
• Applications of Gen AI in Healthcare: Segmentation
• Growth Drivers
• Growth Restraints

Tech Snapshot: Gen AI Applications in Healthcare

• Gen AI for Drug Discovery and Development
• Gen AI for Medical Image Analysis
• Gen AI for Synthetic Data and Digital Twin Creation
• Gen AI for Managing EHRs and Virtual Assistants
• Company Insights for Gen AI in Healthcare: A Snapshot
• Industry Snapshot
• Gen AI in Healthcare: Tech Adoption versus Tech Maturity

Innovation Ecosystem

• Collaboration for Developing Gen AI and Cloud Computing Infrastructure
• Pharmaceutical Companies Significantly Funding Gen AI-based Platform Development for Drug Discovery
• In-hospital Enterprises Significantly Funding the Emerging Market of Healthcare LLMs
• Geographical Distribution of Organizations in Healthcare Gen AI
• Overview of Gen AI Regulations in a Few Prominent Countries

Growth Opportunity Universe

• Growth Opportunity 1: Lighter Clouds to Implement Gen AI in Real Time
• Growth Opportunity 2: Synthetic Data Creation for Virtual Clinical Trials
• Growth Opportunity 3: Healthcare Interoperability Essential to Implement Generative AI

Appendix

• Technology Readiness Levels (TRL): Explanation

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