市场调查报告书
商品编码
1466133
医疗诊断市场中的人工智慧:按组件、技术、应用和最终用户划分 - 2024-2030 年全球预测Artificial Intelligence in Medical Diagnostics Market by Component (Hardware, Services, Software), Technology (Computer Vision, Machine Learning Platforms, Natural Language Processing), Application, End-User - Global Forecast 2024-2030 |
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预计2023年人工智慧医疗诊断市场规模为10亿美元,2024年将达11.6亿美元,2030年将达30.4亿美元,复合年增长率为17.23%。
医疗诊断市场中的人工智慧(AI)包括基于人工智慧的技术和系统的开发、实施和实施,以分析临床资料、识别模式并获得见解,以提高诊断准确性和患者照护。慢性病盛行率的不断上升急剧增加了诊断应用中增强影像分析的需求。越来越多的政府措施促进人工智慧/机器学习技术融入精准医疗和穿戴式设备,这正在加强产品开发,并为市场成长做出重大贡献。然而,产品缺陷的增加以及人工智慧与现有诊断系统整合的困难可能会限制人工智慧诊断解决方案的市场采用。资料隐私和安全漏洞问题已成为市场成长的一个令人担忧的因素。此外,用于医疗诊断的诊断机器人和先进人工智慧技术的引入正在为市场成长创造有吸引力的机会。随着Start-Ups生态系统的发展和智慧医院的扩张,人工智慧技术在医疗诊断中的应用预计将推动市场成长。
主要市场统计 | |
---|---|
基准年[2023] | 10亿美元 |
预测年份 [2024] | 11.6亿美元 |
预测年份 [2030] | 30.4亿美元 |
复合年增长率(%) | 17.23% |
提供多种软体元件以增强组件诊断决策
硬体是AI在医疗诊断领域的关键组成部分,是指AI运算所需的嵌入式系统、感测器、医学影像处理设备等实体设备。感测器和物联网设备是用于收集患者资料并将其发送到人工智慧系统进行分析的主要硬体。硬体环境需要硬体进行资料加密、存取控制以及遵守资料保护条例。服务包括人工智慧医疗诊断的培训、维护、安装和定制,并利用该技术提供远端监控和远端咨询。远端监测包括远距离诊断和持续监测患者的健康状况,对于慢性疾病患者、老年人和偏远地区的人们尤其有利。远端医疗咨询使专家医疗咨询的普及不受地域限制,主要用于农村地区的后续观察、初步诊断和医疗保健。该软体是医疗诊断中人工智慧不可或缺的一部分,利用先进的演算法、机器学习和深度学习模型来分析复杂的医疗资料。它有助于解释扫描影像、识别异常并预测患者预后和治疗反应。软体包括影像分析工具、诊断决策支援系统、基因组分析软体、病理学和显微镜分析等。
技术:电脑视觉技术的重大进步改善了影像分析
电脑视觉涉及训练人工智慧 (AI) 来解释和理解视觉世界。在医学诊断中,该技术使影像导引手术和放射学报告自动解读等程序焕发活力。电脑视觉在放射学和病理学中极为重要,因为这些领域需要解释大量影像资料。机器学习平台使电脑系统能够随着经验的累积而改进,并能够更好地预测疾病进展和早期诊断疾病状况。该技术用于需要持续监测和及时干预的慢性疾病的诊断过程和管理,例如糖尿病和心臟病。自然语言处理(NLP)使人工智慧能够理解和解释人类语言。它对于简化管理业务以及从医疗记录中提取必要的资讯以进行患者照护非常有用。机器人流程自动化 (RPA) 是使用软体机器人来自动执行日常任务,并且可以有效地自动化测试结果、更新患者记录和自动化预约。 RPA 可以自动化大型医院的整个检测过程,消除错误并加快诊断速度。
应用:人工智慧在循环系统领域的应用,提高诊断准确性
人工智慧在循环系统医学领域显示出可喜的成果,包括心臟病的早期检测和治疗。使用人工智慧演算法,医疗专业人员可以根据患者的健康记录和心臟影像来预测心臟麻痹、中风和心臟病的风险。它还成功地标记了心电图 (ECG)资料中的异常情况,帮助医生更准确地诊断节律性心臟疾病。神经系统疾病通常很复杂且难以诊断,人工智慧从大量资料中识别模式的能力使其受益匪浅。透过分析大脑影像扫描并识别人眼错过的细微变化,人工智慧可以在老年痴呆症、帕金森氏症和多发性硬化症等疾病的早期检测中发挥至关重要的作用。利用模式识别,人工智慧可以识别放射影像中可能预示癌症的异常情况,通常可以在肿瘤危及生命之前及早发现它们。人工智慧模型还可用于根据每种癌症的基因组成製定个人化治疗计划。随着计算病理学的快速发展,人工智慧正在彻底改变病理学,因为人工智慧主导的演算法可以立即分析组织样本并检测异常、疾病和感染疾病。透过利用深度学习技术,人工智慧可以评估X光、 电脑断层扫描和MRI扫描等医学影像,以检测肺炎、脑肿瘤和骨折等疾病的征兆。在呼吸医学领域,人工智慧用于预测和管理气喘和慢性阻塞性肺病等慢性疾病,并透过分析电脑断层扫描和解释肺功能测试来帮助早期发现肺癌。在眼科领域,人工智慧演算法被用来诊断各种眼科疾病。深度学习模型可以分析视网膜照片以及早期发现糖尿病视网膜病变,显着降低失明风险。
最终用户:利用人工智慧在医院和诊所进行大规模资料集诊断
在学术机构和研究中心,人工智慧是探索和创新的核心。科学家和研究人员正在利用人工智慧设计早期疾病检测的新方法,促进更快、更有效的诊断,进而实现及时介入。在诊断中心,人工智慧正在透过机器学习模型和影像识别软体彻底改变患者照护,从而增强诊断成像。 AI 演算法可以分析 MRI 扫描、X 光扫描和电脑断层扫描,以检测异常情况并对其进行分类。这些工具有助于更准确的诊断,减少手动错误,并支持及时启动适当的治疗过程。医疗保健作为医疗体系第一线不可或缺的一部分,正在见证着人工智慧在多个方面的令人震惊的融合。主要是,它透过分析患者资料并向医生即时提供重要见解来帮助医生诊断疾病。透过采用人工智慧驱动的工具,医院可以改善传统的患者照护模式,加速诊断过程,并最终改善治疗结果。
区域洞察
在美国,医疗保健人工智慧的大量投资正在带来医疗诊断的突破性研究和进步。美国和加拿大等主要参与者拥有强大的影响力,并拥有透过人工智慧整合彻底改变医疗诊断服务的技术力。欧洲正在兴起为医疗诊断研究和开发提供人工智慧驱动解决方案的新创公司和新兴企业公司。全部区域政府、研究人员和产业参与者之间持续进行的合作活动在推动医疗诊断领域创新市场的成长方面发挥关键作用。亚太地区的主要国家,包括中国、日本和印度,正在帮助该地区的参与者利用其在机器人技术和先进技术方面的专业知识来开发人工智慧主导的诊断工具。由于人口众多、医疗基础设施不断发展以及先进技术的采用增多,亚太地区医疗诊断领域的人工智慧 (AI) 正在经历显着增长。
FPNV定位矩阵
FPNV定位矩阵对于评估医疗诊断市场的人工智慧至关重要。我们检视与业务策略和产品满意度相关的关键指标,以对供应商进行全面评估。这种深入的分析使用户能够根据自己的要求做出明智的决策。根据评估,供应商被分为四个成功程度不同的像限:前沿(F)、探路者(P)、利基(N)和重要(V)。
市场占有率分析
市场占有率分析是一种综合工具,可以对医疗诊断市场人工智慧供应商的现状进行深入而详细的研究。全面比较和分析供应商在整体收益、基本客群和其他关键指标方面的贡献,以便更好地了解公司的绩效及其在争夺市场占有率时面临的挑战。此外,该分析还提供了对该行业竞争特征的宝贵见解,包括在研究基准年观察到的累积、分散主导地位和合併特征等因素。详细程度的提高使供应商能够做出更明智的决策并制定有效的策略,从而在市场上获得竞争优势。
1. 市场渗透率:提供有关主要企业所服务的市场的全面资讯。
2. 市场开拓:我们深入研究利润丰厚的新兴市场,并分析其在成熟细分市场的渗透率。
3. 市场多元化:提供有关新产品发布、开拓地区、最新发展和投资的详细资讯。
4.竞争评估与资讯:对主要企业的市场占有率、策略、产品、认证、监管状况、专利状况、製造能力等进行全面评估。
5. 产品开发与创新:提供对未来技术、研发活动和突破性产品开发的见解。
1.人工智慧在医疗诊断领域的市场规模及预测为何?
2.在医疗诊断市场人工智慧的预测期内,有哪些产品、细分市场、应用程式和领域需要考虑投资?
3. 医疗诊断市场人工智慧的技术趋势和法规结构是什么?
4.医疗诊断人工智慧市场主要厂商的市场占有率是多少?
5. 进入人工智慧医疗诊断市场的合适型态和策略手段是什么?
[189 Pages Report] The Artificial Intelligence in Medical Diagnostics Market size was estimated at USD 1.00 billion in 2023 and expected to reach USD 1.16 billion in 2024, at a CAGR 17.23% to reach USD 3.04 billion by 2030.
Artificial intelligence (AI) in the medical diagnostics market encompasses the development, implementation, and application of AI-based technologies and systems to analyze clinical data, identify patterns, and derive insights for improved diagnostic accuracy and patient care. The increasing prevalence of chronic disease conditions has surged the need for enhanced imaging analysis in diagnostic applications. Rising government initiatives to promote the integration of AI/ML technologies in precision medicine and wearable devices have enhanced product development, significantly contributing to market growth. However, increasing incidences of product failures and the difficulty of AI integration with existing diagnostic systems may limit the market adoption of AI-enabled diagnostic solutions. Data privacy and security breach issues have emerged as concerning factors for market growth. Moreover, the introduction of diagnostic robotics and advanced AI technologies for medical diagnosis has created attractive opportunities for market growth. The advancing start-up ecosystem and expansion of smart hospitals are expected to leverage AI technology in medical diagnostics to bolster the growth of the market.
KEY MARKET STATISTICS | |
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Base Year [2023] | USD 1.00 billion |
Estimated Year [2024] | USD 1.16 billion |
Forecast Year [2030] | USD 3.04 billion |
CAGR (%) | 17.23% |
Component: Availability of a diverse range of software components to offer enhanced diagnostics decision
Hardware is a key component of AI in medical diagnostics which refers to physical devices such as embedded systems, sensors, and medical imaging devices necessitated for AI computation. Sensors and IoT devices are major hardware used to collect patient data and transmit it to AI systems for analysis. Hardware environments require hardware for data encryption, access control, and compliance with data protection regulations. Services include training, maintenance, installation, and customization of AI medical diagnostics, which offer tele-monitoring and tele-consultation using the technology. Telemonitoring includes remote diagnostics and continuous monitoring of the patient's health, particularly beneficial for chronically ill patients, elderly people, and individuals residing in remote areas. Tele-consultation democratizes access to expert medical consultation irrespective of geographical barriers and is predominantly useful for follow-ups, preliminary diagnoses, and rural healthcare. Software forms an integral part of AI in medical diagnostics, leveraging sophisticated algorithms, machine learning, and deep learning models to analyze complex medical data. It helps to interpret scans, identify anomalies, and predict patient prognosis and treatment responses. This software may include image analysis tools, diagnostics decision support systems, genome analysis software, and pathology & microscopy analysis, among others.
Technology: Extensive advancements in computer vision technologies for improved image analysis
Computer vision involves training artificial intelligence (AI) to interpret and understand the visual world. In medical diagnostics, this technology has revitalized procedures such as image-guided surgeries and automated reading of radiology reports. Computer vision is crucial in radiology and pathology, where large volumes of image data are interpreted. Machine Learning platforms enable computer systems to improve with experience, and they excel in predicting disease progression and diagnosing conditions at early stages. This technology is used in the diagnosis process and management of chronic diseases such as diabetes or heart disease, which require continuous monitoring and timely interventions. Natural language processing (NLP) allows AI to understand and interpret human language. It is effective in streamlining administrative tasks and extracting essential information from medical records for patient care. Robotic process automation (RPA) is leveraging software robots to automate routine tasks and is efficient in automating laboratory results, updating patient records, and booking appointments. RPA can automate the entire laboratory process in large-scale hospitals, eliminating errors and speeding up diagnoses.
Application: Adoption of AI in cardiology segment to enhance diagnostic accuracy
Artificial intelligence has shown promising results in cardiology, including the early detection and treatment of heart diseases. Using AI algorithms, medical professionals can predict a patient's risk of cardiac arrest, strokes, and heart disease based on their health records and cardiac images. It has also been successful in flagging anomalies in electrocardiogram (ECG) data, aiding doctors in diagnosing rhythmic heart disorders more accurately. Neurological disorders, often complex and difficult to diagnose, significantly benefit from AI's capacity to recognize patterns in voluminous data. AI is pivotal in the early detection of conditions such as Alzheimer's, Parkinson's, and multiple sclerosis by analyzing brain imaging scans and identifying minute changes that the human eye may overlook. Using pattern recognition, AI can identify abnormalities in radiology images that can indicate cancer, often catching early-stage tumors before they become more life-threatening. AI models can also be utilized to formulate personalized treatment plans based on individual cancer genetic makeup. AI has revolutionized pathology by speeding up disease diagnostics with the surge of computational pathology, as AI-driven algorithms can instantaneously analyze tissue samples to detect abnormalities, diseases, and infections. Utilizing deep learning techniques, AI can evaluate medical images such as X-rays, CT scans, and MRI scans to detect signs of diseases, including pneumonia, brain tumors, and fractures. In pulmonology, AI is used to predict and manage chronic conditions such as asthma and COPD, and it helps with the early detection of lung cancer via the analysis of CT scans and interpretation of pulmonary function tests. Ophthalmology uses AI algorithms for diagnosing various eye diseases. Deep learning models can analyze retinal photos to detect diabetic retinopathy in its early stages, significantly reducing the risk of blindness.
End-User: Utilization of AI for large data set diagnosis in hospitals and clinics
Within academic institutions and research centers, AI is a focal point of exploration and innovation. Scientists and researchers leverage AI to devise new methodologies for early disease detection, facilitating faster and more efficient diagnosis and, in turn, enabling timely intervention. In diagnostic centers, AI is revolutionizing patient care with machine learning models and image recognition software, enabling enhanced diagnostic imaging. AI algorithms can analyze MRI scans, X-rays, and CT scans to detect and classify anomalies; this includes even minor abnormalities that can often escape unaided human interpretation. These tools facilitate more accurate diagnoses and reduce the scope of manual errors, supporting the timely beginning of an appropriate course of treatment. Hospitals, integral parts of the frontline healthcare system, are witnessing an impactful integration of AI in various capacities. Predominantly, it assists physicians in disease diagnosis by analyzing patient data and presenting key insights to the physician in real-time. By adopting AI-powered tools, hospitals can improve upon traditional patient care models, expedite the diagnosis process, and ultimately deliver improved treatment outcomes.
Regional Insights
Significant investments in AI for healthcare in the United States have led to groundbreaking research and advancements in medical diagnostics. Major countries such as the United States and Canada have the strong presence of key players equipped with technological capabilities to revolutionize medical diagnostics services through artificial intelligence integration. Europe has witnessed the emergence of several startups and established companies producing AI-driven solutions for research & development in medical diagnostics. Ongoing collaboration activities between governments, researchers, and industry players across the EMEA region are playing a crucial role to drive innovation market growth in the medical diagnostics sector. Significant countries in the APAC region, including China, Japan, and India, support regional players to leverage their expertise in robotics and advanced technologies to develop AI-driven diagnostic tools. Artificial Intelligence (AI) in medical diagnostics in the APAC has witnessed significant growth owing to its large population base, evolving healthcare infrastructure, and increasing adoption of advanced technologies.
FPNV Positioning Matrix
The FPNV Positioning Matrix is pivotal in evaluating the Artificial Intelligence in Medical Diagnostics Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
Market Share Analysis
The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Artificial Intelligence in Medical Diagnostics Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
Key Company Profiles
The report delves into recent significant developments in the Artificial Intelligence in Medical Diagnostics Market, highlighting leading vendors and their innovative profiles. These include 3M Company, AiCure, LLC, Aidoc Medical Ltd., Butterfly Network, Inc., Cera Care Limited, Cisco Systems, Inc., Corti - AI, Digital Diagnostics Inc., Edifecs, Inc., Enlitic, Inc., Epredia by PHC Holdings Corporation, Freenome Holdings, Inc., GE HealthCare Technologies, Inc., General Vision, Inc., Google LLC by Alphabet Inc., Hewlett Packard Enterprise Development LP, Imagen Technologies, Inc., Intel Corporation, International Business Machines Corporation, Johnson & Johnson Services, Inc., Kantify, Koninklijke Philips N.V., Medtronic PLC, Microsoft Corporation, Nano-X Imaging Ltd., NEC Corporation, NVIDIA Corporation, Persistent Systems Limited, Qure.ai Technologies Private limited, Siemens Healthineers AG, SigTuple Technologies Private Limited, Stryker Corporation, Tempus Labs, Inc., and VUNO Inc..
Market Segmentation & Coverage
1. Market Penetration: It presents comprehensive information on the market provided by key players.
2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.
1. What is the market size and forecast of the Artificial Intelligence in Medical Diagnostics Market?
2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Artificial Intelligence in Medical Diagnostics Market?
3. What are the technology trends and regulatory frameworks in the Artificial Intelligence in Medical Diagnostics Market?
4. What is the market share of the leading vendors in the Artificial Intelligence in Medical Diagnostics Market?
5. Which modes and strategic moves are suitable for entering the Artificial Intelligence in Medical Diagnostics Market?