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시장보고서
상품코드
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의료 진단용 인공지능(AI) 시장 : 시장 규모, 점유율, 성장 분석 - 컴퍼넌트별, 모달리티별, 용도별, 최종 용도별, 지역별 - 산업 예측(2025-2032년)Artificial Intelligence In Medical Diagnostics Market Size, Share, Growth Analysis, By Component (Hardware, Software), By Modality (Imaging Modalities, Diagnostic Modalities), By Application, By End Use, By Region - Industry Forecast 2025-2032 |
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세계의 의료 진단용 인공지능 시장 규모는 2023년 17억 달러로 평가되었고, 2024년 21억 달러에서 2032년 114억 4,000만 달러로 성장할 전망이며, 예측기간 2025년부터 2032년까지 CAGR 23.6%로 성장할 전망입니다.
의료 진단용 인공지능(AI) 시장은 정확한 진단에 대한 요구가 높아지고, 자격을 갖춘 건강관리 전문가 부족, 만성질환 유병률 증가 등을 배경으로 상당한 성장이 예상됩니다. AI 소프트웨어 및 서비스는 딥러닝과 고급 알고리즘을 활용하여 진단 프로세스를 강화하고, 중요한 질병을 확인함으로써 진단 실수를 줄이고 의료진의 부담을 완화시켜 줍니다. 시장의 확대는 신흥 국가의 미개척 가능성과 혁신과 효율성을 촉진하는 이업종간 파트너십의 급증에 의해 더욱 지원되고 있습니다. 헬스케어 시스템이 점점 AI 기술을 채택함에 따라 AI 구동형 진단 도구의 수요는 확대될 것으로 예상되어 의료기술 분야의 이해관계자들에게 큰 비즈니스 기회를 가져다 주고 있습니다.
Global Artificial Intelligence In Medical Diagnostics Market size was valued at USD 1.7 billion in 2023 and is poised to grow from USD 2.10 billion in 2024 to USD 11.44 billion by 2032, growing at a CAGR of 23.6% during the forecast period (2025-2032).
The market for artificial intelligence (AI) in medical diagnostics is poised for substantial growth, driven by the rising need for accurate diagnoses, a shortage of qualified healthcare professionals, and the increasing prevalence of chronic diseases. AI software and services enhance diagnostic processes by leveraging deep learning and advanced algorithms to identify critical disorders, thereby reducing diagnostic errors and alleviating the burden on medical staff. The market's expansion is further supported by the untapped potential in emerging economies and a surge in cross-industry partnerships that foster innovation and efficiency. As healthcare systems increasingly adopt AI technologies, the demand for AI-driven diagnostic tools is expected to grow, presenting significant opportunities for stakeholders in the medical technology sector.
Top-down and bottom-up approaches were used to estimate and validate the size of the Global Artificial Intelligence In Medical Diagnostics market and to estimate the size of various other dependent submarkets. The research methodology used to estimate the market size includes the following details: The key players in the market were identified through secondary research, and their market shares in the respective regions were determined through primary and secondary research. This entire procedure includes the study of the annual and financial reports of the top market players and extensive interviews for key insights from industry leaders such as CEOs, VPs, directors, and marketing executives. All percentage shares split, and breakdowns were determined using secondary sources and verified through Primary sources. All possible parameters that affect the markets covered in this research study have been accounted for, viewed in extensive detail, verified through primary research, and analyzed to get the final quantitative and qualitative data.
Global Artificial Intelligence In Medical Diagnostics Market Segmental Analysis
Global Artificial Intelligence In Medical Diagnostics Market is segmented by component, modality, application, end use and region. Based on component, the market is segmented into hardware, software and services. Based on modality, the market is segmented into imaging modalities and diagnostic modalities. Based on application, the market is segmented into in vivo diagnostics and in vitro diagnostics. Based on end use, the market is segmented into hospitals, diagnostic imaging centers, diagnostic laboratories and others. Based on region, the market is segmented into North America, Europe, Asia Pacific, Latin America and Middle East & Africa.
Driver of the Global Artificial Intelligence In Medical Diagnostics Market
The Global Artificial Intelligence in Medical Diagnostics market is significantly driven by the increasing integration of Big Data into healthcare. As digitization and advanced information systems become commonplace, a vast array of complex data is generated throughout the care delivery process. This includes diverse sources such as medical device readings, patient interactions on digital platforms, and biometric records. The past decade has seen remarkable advancements in Big Data analytics, fueled by widespread adoption of electronic health records, digitized laboratory slides, and high-resolution imaging. Furthermore, the rise of bidirectional patient portals, enabling patients to contribute data to their electronic medical records, is set to enhance the data volume in medical diagnostics, thereby intensifying the need for AI-based solutions to efficiently manage these growing data streams.
Restraints in the Global Artificial Intelligence In Medical Diagnostics Market
The Global Artificial Intelligence in Medical Diagnostics market faces a notable restraint due to the skepticism surrounding AI accuracy among medical professionals. Despite advancements in digital health that equip doctors with innovative tools for patient care, many practitioners remain hesitant to embrace these technologies. A prevailing concern is the misconception that AI could replace physicians, undermining vital human skills such as empathy and persuasion. This hesitation is compounded by fears of patients becoming overly dependent on technology, potentially neglecting essential in-person care and disrupting established doctor-patient relationships. As a result, convincing healthcare providers of the safety, efficacy, and cost-effectiveness of AI solutions becomes a challenge. Nonetheless, as awareness of the benefits of AI in medical diagnostics grows, it is likely that acceptance of these technologies will increase among doctors and radiologists in the future.
Market Trends of the Global Artificial Intelligence In Medical Diagnostics Market
The Global Artificial Intelligence in Medical Diagnostics market is witnessing a significant upward trend as developers strive to create more human-aware AI systems that align closely with human cognitive processes. Despite facing challenges in building interactive, scalable machines and automating complex components, the increasing demand for advanced diagnostic tools drives innovation. The need for efficient interpretation and presentation of machine-learning outcomes fuels ongoing research, focusing on enhancing user experience and feedback mechanisms. Moreover, as healthcare providers seek improvements in diagnostic accuracy and operational efficiency, the market for AI-powered solutions is poised for substantial growth, presenting lucrative opportunities for developers and stakeholders alike.