![]() 17, 18 Other health care imaging applications in development include classification of gross dermatology images, 19 radiology images, 20 and pathology images. One of the first machine learning applications to be approved for clinical use interprets funduscopic images automatically for assessment of diabetic retinopathy. The new machine learning techniques and data resources stimulated a wave of research and development exploring the use of machine learning applications in a variety of domains, including health care. 11, 12 These efforts and the resources they produce are intended to ensure that new products fit health care needs and are implemented in a way that supports care quality. The US Food and Drug Administration (FDA) is collaborating with the American College of Radiology on use case development and is considering methods for evaluating and validating AI software and devices that ensure quality while allowing innovation and optimization. ![]() 9 The American College of Radiology has established a Data Science Institute 10 that has created a set of use cases for AI in radiology to guide and promote the development of AI tools for radiology practice. 5 The Canadian Association of Radiologists published a white paper on AI in radiology. The American Medical Association (AMA) has created AI education resources and has released a policy statement with recommendations for the development and deployment of AI systems. ![]() 8Īs prototype AI products develop, professional organizations and government agencies are attempting to clarify use cases and regulatory strategies for AI in healthcare. ![]() 4– 6 However, news from initial AI implementations has not been uniformly good: a 2017 industry survey indicated that half of early adopters encountered immature AI products that did not yield anticipated benefits, 7 a situation perhaps best exemplified by the failure of IBM's Watson Oncology at the MD Anderson Cancer Center (Houston, Texas). A number of reports and white papers have addressed potential benefits and disruptions in health care from AI, with a generally positive perspective. 3 Although AI technologies have not yet had a large impact on clinical practice, the introduction of software and devices with significant new capabilities could disrupt existing workflows, practice patterns, and reimbursement policies in unpredictable ways. AI publications in the biomedical literature have increased more than 8-fold since 2000 ( Figure 1), and the total market for AI in health care is expected to grow from $856 million in 2017 to more than $20 billion by 2025, including both software and hardware. Health care is an important domain for AI growth. Industry projections variously estimate the global AI market to exceed $100 billion to $300 billion by 2025, with an annual growth of 40% to 50%. Artificial intelligence (AI) is a focus of intense scientific, business, and governmental interest: global AI scientific publications have increased more than 6-fold in the past 20 years to more than 60 000 annually, 1 at least 26 governments have established national AI strategies during the past 4 years, 2 and daily articles in the business and lay press discuss the future applications and impact of AI.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |