Introduction

Artificial intelligence (AI) is the scientific discipline that develops computer algorithms and machines that apply the algorithms to perform tasks that require human intelligence. The origins of AI can be traced to Alan Turing who proposed the question “Can machines think?” and argued that machines can indeed think intelligently. While it is arguable if machines can think, it is undoubtedly clear today that AI can perform intelligent tasks as in speech recognition, machine translation, autonomous planning and scheduling, image understanding, robotic vehicles, recommender systems, and game playing. In health care, AI is increasingly leveraged for more precise risk assessment and prognostication, faster and accurate diagnosis, selection of appropriate therapy, reduction of errors due to human fatigue, patient monitoring, reduction in medical costs and robotic surgery.

The rapid pace of AI development and accelerated regulatory approval by the FDA has resulted in a range of AI that has been introduced in clinical use. Going forward, physicians will increasingly work with AI, and will need to be educated to procure the benefits and mitigate the risks of AI.

AI in medical curriculum

Future physicians will need a broad range of skills to effectively use AI in clinical practice. A pragmatic approach is to aim for AI literacy rather than proficiency, just as, a physician utilizing MRI does not need to be proficient in particle spin physics. Specifically, medical students will need to be knowledgeable in using, interpretating and explaining AI:

• Using AI – understand what AI is, when it is appropriate to use in a given clinical context, and what inputs are required to obtain meaningful results
• Interpreting AI – understand and appraise the accuracy of results from an AI, as well as sources of error, bias, or clinical inapplicability
• Explaining AI – communicate in results and the processes underlying AI to stakeholders such as patients, families, and allied health professionals

Objectives

1. To understand the basic concepts of data science, clinical informatics, ML, and AI.
2. To understand the role of AI/ML in medicine.
3. To appreciate the applications of AI/ML across different medical specialties.
4. To appreciate specific examples of AI/ML in biomedical research, medical imaging, acute care medicine, and primary care.
5. To understand how AI can improve healthcare delivery.

Sessions

• Session 1: Introduction to Data Science and Clinical Informatics
• Session 2: Overview of AI/ML approaches across healthcare
• Session 3: ML to deconvolute systems biology
• Session 4: AI in medical imaging
• Session 5: AI in cancer/oncology
• Session 6: AI to improve in-hospital healthcare delivery

Literature

Education
McCoy LG, Nagaraj S, Morgado F, Harish V, Das S, Celi LA. What do medical students actually need to know about Artificial Intelligence? NPJ Digit Med. 2020 Jun 19;3:86. doi: 10.1038/s41746-020-0294-7. PMID: 32577533; PMCID: PMC7305136. (paper)

Paranjape K, Schinkel M, Nannan Panday R, Car J, Nanayakkara P. Introducing Artificial Intelligence training in medical education. JMIR Med Educ. 2019 Dec 3;5(2):e16048. doi: 10.2196/16048. PMID: 31793895; PMCID: PMC6918207. (paper)

Grunhut J, Wyatt AT, Marques O. Educating future physicians in Artificial Intelligence (AI): An integrative review and proposed changes. J Med Educ Curric Dev. 2021 Sep 6;8:23821205211036836. doi: 10.1177/23821205211036836. PMID: 34778562; PMCID: PMC8580487. (paper)

Reading AI articles
Liu Y, Chen PC, Krause J, Peng L. How to read articles that use machine learning: Users' guides to the medical literature. JAMA. 2019 Nov 12;322(18):1806-1816. doi: 10.1001/jama.2019.16489. PMID: 31714992. (paper)

Overview of AI in medicine
Topol EJ. High-performance medicine: The convergence of human and Artificial Intelligence. Nat Med. 2019 Jan;25(1):44-56. doi: 10.1038/s41591-018-0300-7. Epub 2019 Jan 7. PMID: 30617339. (paper)

Bias
Parikh RB, Teeple S, Navathe AS. Addressing bias in Artificial Intelligence in health care. JAMA. 2019 Dec 24;322(24):2377-2378. doi: 10.1001/jama.2019.18058. PMID: 31755905. (paper)

Trust
Asan O, Bayrak AE, Choudhury A. Artificial Intelligence and human trust in healthcare: Focus on clinicians. J Med Internet Res. 2020 Jun 19;22(6):e15154. doi: 10.2196/15154. PMID: 32558657; PMCID: PMC7334754. (paper)

Reporting
Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for Artificial Intelligence in health care. J Am Med Inform Assoc. 2020 Dec 9;27(12):2011-2015. doi: 10.1093/jamia/ocaa088. PMID: 32594179; PMCID: PMC7727333. (paper)

FDA approved AI
Benjamens S, Dhunnoo P, Meskó B. The state of Artificial Intelligence-based FDA-approved medical devices and algorithms: An online database. NPJ Digit Med. 2020 Sep 11;3:118. doi: 10.1038/s41746-020-00324-0. PMID: 32984550; PMCID: PMC7486909. (paper) (Database: https://medicalfuturist.com/fda-approved-ai-based-algorithms/)

Pathology
Hartman DJ, Van Der Laak JAWM, Gurcan MN, Pantanowitz L. Value of Public Challenges for the Development of Pathology Deep Learning Algorithms. J Pathol Inform. 2020 Feb 26;11:7. doi: 10.4103/jpi.jpi_64_19. PMID: 32318315; PMCID: PMC7147520. (paper)

Ethics in intensive care
Shaw JA, Sethi N, Block BL. Five Things Every Clinician Should Know About AI Ethics in Intensive Care. Intensive Care Med. 2021 Feb;47(2):157-159. doi: 10.1007/s00134-020-06277-y. Epub 2020 Oct 19. PMID: 33078241; PMCID: PMC7571864. (paper)