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Artificial Intelligence in Primary Health Care: Perceptions, Issues, and ChallengesPrimary Health Care Informatics Working Group Contribution to the Yearbook of Medical Informatics 2019
25 April 2019 (online)
Background: Artificial intelligence (AI) is heralded as an approach that might augment or substitute for the limited processing power of the human brain of primary health care (PHC) professionals. However, there are concerns that AI-mediated decisions may be hard to validate and challenge, or may result in rogue decisions.
Objective: To form consensus about perceptions, issues, and challenges of AI in primary care.
Method: A three-round Delphi study was conducted. Round 1 explored experts’ viewpoints on AI in PHC (n=20). Round 2 rated the appropriateness of statements arising from round one (n=12). The third round was an online panel discussion of findings (n=8) with the members of both the International Medical Informatics Association and the European Federation of Medical Informatics Primary Health Care Informatics Working Groups.
Results: PHC and informatics experts reported AI has potential to improve managerial and clinical decisions and processes, and this would be facilitated by common data standards. The respondents did not agree that AI applications should learn and adapt to clinician preferences or behaviour and they did not agree on the extent of AI potential for harm to patients. It was more difficult to assess the impact of AI-based applications on continuity and coordination of care.
Conclusion: While the use of AI in medicine should enhance healthcare delivery, we need to ensure meticulous design and evaluation of AI applications. The primary care informatics community needs to be proactive and to guide the ethical and rigorous development of AI applications so that they will be safe and effective.
KeywordsMedical record systems, computerised - privacy - general practice - Delphi technique - Artificial Intelligence
- 1 Beard JR, Bloom DE. Towards a comprehensive public health response to population ageing. Lancet 2015; 385 (9968) 658-61
- 2 Starfield B, Shi L, Macinko J. Contribution of primary care to health systems and health. The Milbank Q 2005; 83 (03) 457-502
- 3 Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; Nov 27 19 (06) 1236-46
- 4 Xiao C, Choi E, Sun J. Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review. J Am Med Inform Assoc 2018; 25 (10) 1419-28
- 5 Powles J, Hodson H. Google DeepMind and healthcare in an age of algorithms. Health Technol (Berl) 2017; 7 (04) 351-67
- 6 Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G. et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016; 529: 484
- 7 The LJL. Artificial intelligence in health care: within touching distance. 2018 390. (10114) 2739
- 8 Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med 2018; 1 (01) 39
- 9 Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316 (22) 2402-10
- 10 Keane PA, Topol EJ. With an eye to AI and autonomous diagnosis. NPJ Digi Med 2018; 1 (01) 40
- 11 Stead WW. Clinical implications and challenges of artificial intelligence and deep learning. JAMA 2018; 320 (11) 1107-8
- 12 Char DS, Shah NH, Magnus D. Implementing Machine Learning in Health Care — Addressing Ethical Challenges. N Engl J Med 2018; 378 (11) 981-3
- 13 Sullivan HR, Schweikart SJ. Are Current Tort Liability Doctrines Adequate for Addressing Injury Caused by AI?. AMA J Ethics 2019; 21 (02) 160-6
- 14 Fitch K, Bernstein SJ, Aguilar MD, Burnand B, LaCalle JR. The RAND/UCLA appropriateness method user’s manual. RAND CORP SANTA MONICA CA 2001
- 15 Alanazi HO, Abdullah AH, Qureshi KN. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care. J Med Syst 2017; 41 (04) 69
- 16 Sullivan K, ElMolla A, Squires B, Luke S. editors. Unlearning from demonstration. Twenty-Third International Joint Conference on Artificial Intelligence. 2013
- 17 Ashraf A, Khan S, Bhagwat N, Chakravarty M, Taati B. Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies. arXiv preprint arXiv:181201716 2018
- 18 Blease C, Bernstein MH, Gaab J, Kaptchuk TJ, Kossowsky J, Mandl KD. et al. Computerization and the future of primary care: A survey of general practitioners in the UK. PloS One 2018; 13 (12) e0207418