Computerization of the Work of General Practitioners: Mixed Methods Survey of Final-Year Medical Students in Ireland
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Abstract
Background: The potential for digital health technologies, including machine learning (ML)–enabled tools, to disrupt the medical profession is the subject of ongoing debate within biomedical informatics. Objective: We aimed to describe the opinions of final-year medical students in Ireland regarding the potential of future technology to replace or work alongside general practitioners (GPs) in performing key tasks. Methods: Between March 2019 and April 2020, using a convenience sample, we conducted a mixed methods paper-based survey of final-year medical students. The survey was administered at 4 out of 7 medical schools in Ireland across each of the 4 provinces in the country. Quantitative data were analyzed using descriptive statistics and nonparametric tests. We used thematic content analysis to investigate free-text responses. Results: In total, 43.1% (252/585) of the final-year students at 3 medical schools responded, and data collection at 1 medical school was terminated due to disruptions associated with the COVID-19 pandemic. With regard to forecasting the potential impact of artificial intelligence (AI)/ML on primary care 25 years from now, around half (127/246, 51.6%) of all surveyed students believed the work of GPs will change minimally or not at all. Notably, students who did not intend to enter primary care predicted that AI/ML will have a great impact on the work of GPs. Conclusions: We caution that without a firm curricular foundation on advances in AI/ML, students may rely on extreme perspectives involving self-preserving optimism biases that demote the impact of advances in technology on primary care on the one hand and technohype on the other. Ultimately, these biases may lead to negative consequences in health care. Improvements in medical education could help prepare tomorrow’s doctors to optimize and lead the ethical and evidence-based implementation of AI/ML-enabled tools in medicine for enhancing the care of tomorrow’s patients.
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Rather than replacing human physicians, students perceived AI as a collaborative partner in medical practice.
“Generally, rather than replacing human physicians, students perceived AI as a collaborative partner in medical practice [73, 74].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [73]
The Results (Quantitative Survey; page unavailable) report that only 35–37% of students expected GPs to routinely work in partnership with AI/ML on diagnosis, prognosis, or treatment plans. The Discussion (Summary of the Major Findings; page unavailable) likewise calls those forecasting such a partnership a minority, contrary to the claim that students generally perceived AI as a collaborative partner. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredStudents generally expressed enthusiasm for integrating AI into their future medical practice, many believed AI had the potential to enhance and transform medicine by optimizing physicians' workloads and reducing healthcare costs, and students viewed AI as a supportive tool that can assist physicians, help mitigate medical errors, and enhance patient care.
“Overall, in addition to positive attitudes toward AI, students generally expressed enthusiasm for integrating it into their future medical practice [70, 71]. Many believed AI had the potential to enhance and transform medicine by optimizing physicians’ workloads and reducing healthcare costs [42, 72, 73]. Additionally, students viewed AI as a supportive tool that can assist physicians, help”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [73]
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- Authors
- Blease, C., Kharko, A., Bernstein, M., Bradley, C., Houston, M., Walsh, I., & D Mandl, K.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Uppsala University (SE)
- Brown University (US)
- University College Cork (IE)
- Ollscoil na Gaillimhe – University of Galway (IE)
- Queen's University Belfast (GB)
- Corresponding author
- Blease, Charlotte
- ORCID
- Venue
- JMIR Medical Education
- Publisher
- JMIR Publications Inc.
- Published
- 2023-03-20
- Volume
- 9
- Pages
- e42639
- ISSN
- 2369-3762 (ISSN-L)
- PMC
- PMC10131917
- OpenAlex
- W4316335339
- Cited by
- 10
- References
- 38
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 2
- FWCI
- 0.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Research Portal (Queen's University Belfast) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Lenus, The Irish Health Repository (Dr Steevens Hospital Library) (submittedVersion)
- Funders
- University College Dublin
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Keywords
- artificial intelligence in healthcare, machine learning, general practitioners, primary care, medical students' perceptions, future of primary care, optimism bias, medical education, digital health technologies
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