Table 5.

Curriculum focus and objectives.

Themes (framed by McCoy et al [43]) and topic		Description	Number of studies	References
Using AIa
	Fundamentals of AI	An overview of all stages of model development, translation, and use in clinical practice. Specifically, this would cover nomenclature and principles such as data collection and transformation, algorithm selection, model development, training and validation, and interpreting model output	20	[5,26,27,32,34,36,37,39-41,43,46,47,51,52,55,57-59,62]
	Fundamentals of health care data science	Fundamental understanding of the environment supported by AI. This includes an overview of biostatistics, big data, data streams available, and how algorithms and machine learning use and process data	20	[5,13,26-36,41,42,45,49,51,52,54]
	Fundamentals of biomedical informatics	An overview of essential concepts such as nomenclature (information and knowledge taxonomy), structure and function of computers, information and communications technology, standards in biomedical informatics, and technology evaluation	1	[39]
	Multidisciplinary collaboration	Learning how to partner and communicate with experts in engineering and data science to ensure clinical relevance and accuracy of AI systems	13	[26,29,31,33,43,45,51-54,57,58,62]
	Applications of AI	Providing examples of AI that have been implemented in health care settings to understand the impact of technologies that incorporate AI	11	[2,32,39,40,44-46,51,52,55,57]
	Implementation of AI in health care settings	Understanding how to embed AI tools into clinical settings and workflows. Specifically, this includes requirements for clinical translation and interpretation of model outputs	9	[27,30,32-34,41,45,57,62]
	Strengths and limitations of AI	Understanding the value, pitfalls, weaknesses and potential errors or unintended consequences that may occur when using AI tools	13	[26,30,32-34,37,41,45,51,52,55,58,62]
	Ethical considerations	Understanding and building awareness of ethics, equity, inclusion, patient rights, and confidentiality when using AI tools	13	[5,26,28-30,33,36,39,41,42,46,54,58]
	Legal considerations and governance strategy	Understanding data governance principles, regulatory frameworks, legislation, policy on using data and AI tools, as well as liability or intellectual property issues	7	[27,30,39,41,45,51,58]
	Economic considerations	“Understanding of how business or clinical processes will be altered through the integration of AI technologies into health care” [58] as well as commercialization	2	[26,33]
Interpreting results from AI
	Medical decision-making	Understanding decision science and probabilities from AI diagnostic and therapeutic algorithms to then meaningfully apply them in clinical decision-making	8	[13,26,28-31,39,51]
	Data visualization	Understanding how to present and describe outputs from AI tools	4	[27,30,52,54]
	Product development projects	Hands on experience to develop, test, and validate AI algorithms with real medical data	2	[52,54]
Explaining results from AI
	Communicating with patients	Mastering how to communicate results with patients in a personalized and meaningful way and discuss the use of AI in the medical decision-making process	8	[5,28-30,32,36,43,46]
	Compassion and empathy	Cultivating and expressing empathy and compassion when communicating with patients	4	[28-30,36]
	Critical appraisal	Understanding how to evaluate AI diagnostic and therapeutic algorithms	7	[2,34,40,43,51,54,59]

^aAI: artificial intelligence.