The impact of artificial intelligence on clinical education: perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers

Abstract

Background: Artificial intelligence (AI) technologies are increasingly used in clinical practice. Although there is robust evidence that AI innovations can improve patient care, reduce clinicians' workload and increase efficiency, their impact on medical training and education remains unclear.

Methods: A survey of trainee doctors' perceived impact of AI technologies on clinical training and education was conducted at UK NHS postgraduate centers in London between October and December 2020. Impact assessment mirrored domains in training curricula such as 'clinical judgement', 'practical skills' and 'research and quality improvement skills'. Significance between Likert-type data was analysed using Fisher's exact test. Response variations between clinical specialities were analysed using k-modes clustering. Free-text responses were analysed by thematic analysis.

Results: Two hundred ten doctors responded to the survey (response rate 72%). The majority (58%) perceived an overall positive impact of AI technologies on their training and education. Respondents agreed that AI would reduce clinical workload (62%) and improve research and audit training (68%). Trainees were skeptical that it would improve clinical judgement (46% agree, p = 0.12) and practical skills training (32% agree, p < 0.01). The majority reported insufficient AI training in their current curricula (92%), and supported having more formal AI training (81%).

Conclusions: Trainee doctors have an overall positive perception of AI technologies' impact on clinical training. There is optimism that it will improve 'research and quality improvement' skills and facilitate 'curriculum mapping'. There is skepticism that it may reduce educational opportunities to develop 'clinical judgement' and 'practical skills'. Medical educators should be mindful that these domains are protected as AI develops. We recommend that 'Applied AI' topics are formalized in curricula and digital technologies leveraged to deliver clinical education.

Keywords: Artificial intelligence; Clinical training; Machine learning; Medical education.

Fig. 1

Domain-based impact of clinical AI on training and education - waffle plots of responses to Likert-type questions in survey of 210 trainee doctors (each icon represents one respondent to the survey)

Fig. 2

Exposure to AI systems and attitudes towards AI training - waffle plots of responses to Likert-type questions in survey of 210 trainee doctors (each icon represents one respondent to the survey)

Fig. 3

The Likert-type statement responses cluster composition of different clinical specialities. (Acute specialities include Acute Medicine, Intensive Care Medicine, Anaesthetics and Emergency Medicine; Child and maternal health include Paediatrics, Obstetrics and Gynaecology; Community specialities include General Practice and Psychiatry)

Fig. 4

Themes and sub-themes identified from thematic analysis of free-text response data, along with representative examples from the raw data

Fig. 5

Potential new AI-related clinical training domains in future curricula. Novel training and assessment methods to deliver AI-based training. (ML = machine learning). (Created with BioRender.com)