JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is reshaping health care, yet educational efforts have focused largely on preparing clinicians to work with AI rather than on preparing the computational scientists who design these systems to work within health systems. Framing this data work as care work foregrounds its ethical and clinical consequences.

Objective:

To map the literature on education, training, and professional development for computational scientists in health, including the competencies taught, engagement with clinicians and patients, attention to ethics, and evaluation of outcomes.

Methods:

We followed the Joanna Briggs Institute (JBI) methodology for scoping reviews and reported according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews); the protocol was registered on the Open Science Framework.Ovid MEDLINE, APA PsycINFO, ERIC (Education Resources Information Center), Scopus, and IEEE Xplore were searched for published sources from 2015 to September 15, 2026. Eligibility was defined using the Population, Concept, and Context framework. After calibration on a dual-screened subset (Cohen κ=0.61), a single reviewer completed screening and charting in Covidence. Data were summarized using descriptive statistics and exploratory thematic analysis.

Results:

Of 7,242 records screened, 44 studies were included. Half were conducted in the United States (22/44, 50%), and informatics journals were the most common venue (15/44, 34%). Studies primarily described curricula (15/44, 34%), identified competencies (12/44, 27%), described experiential learning (10/44, 23%), or analyzed the workforce (4/44, 9%). Health domain competencies were described in 40 (91%) studies, and 35 (80%) characterized their programs as interdisciplinary or multidisciplinary. However, structured exchange with clinical knowledge holders was less common: clinical exposure (12/44, 27%), cross-faculty delivery (8/44, 18%), and patient involvement (5/44, 11%). Ethics was referenced in 30 (68%) studies, yet only 4 (9%) described a dedicated ethics course, and responsible AI accounted for 12 (5%) of 248 extracted competencies. Thirteen (30%) studies acknowledged algorithmic bias; 6 (14%) described educational activities to address it. Communication deficits were noted in 28 (64%) studies, but only 8 (18%) described corresponding curricular content. We identified 78 distinct job titles. Only 14 (32%) studies evaluated learning outcomes, predominantly through learner self-report; none assessed changes in practice.

Conclusions:

Educational effort is asymmetric: clinicians are increasingly taught to understand AI, whereas the computational scientists who build it are seldom taught to understand health care. Ethics and equity are endorsed as competencies but rarely resourced as curriculum. Educational investment should be rebalanced toward the computational workforce, through a shared core competency framework - spanning health system literacy, communication and collaboration with clinicians and patients, responsible AI, and project and change management - that is differentiated by role, delivered through structured clinical and patient engagement, and evaluated beyond learner satisfaction. Clinical Trial: Registration: Open Science Framework; https://osf.io/8nyd9