Improving the quality of machine learning in health applications and clinical research
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Implementation of clinical AI depends on interoperability, user acceptance, and safeguards to prevent workflow disruption and unintended consequences.
“Implementation depends on interoperability, user acceptance, and safeguards to prevent workflow disruption and unintended consequences [40].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [40]
In “No success without an appreciation of context” (page not available), the paper says clinical deployment requires integrating predictions with other clinical information, accounting for clinical workflow, and working with end-users. It does not specifically identify interoperability, user acceptance, or safeguards against workflow disruption and unintended consequences as implementation requirements. (LLM, conf=0.84)
Experimental — accuracy not yet measuredInterpretable models, such as generalised additive models and rule-based systems, provide transparent representations that allow clinicians to understand how variables contribute to predictions.
“Interpretable models, such as generalised additive models and rule-based systems, provide transparent representations that allow clinicians to understand how variables contribute to predictions [40].”
From Artificial Intelligence in Clinical Decision-Making: A Comprehensive Review of Diagnostic, Prognostic, and Therapeutic Applications, Validation Gaps, Bias, and Deployment Challenges (2026) · cites this paper as [40]
The sections ‘The role of reporting guidelines’ and ‘Development cycle of ML/AI solutions for clinical care’ discuss transparent reporting of model methods, not inherently interpretable models. Searches for generalised additive models, rule-based systems, and clinicians understanding variable contributions found no passage addressing the claim. No page numbers are available in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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References 14 · Cited by 61
Research works printed but not linked: 7
- Ben-Israel, D. et al. Artif. Intell. Med. 103, 101785 (2020).
- Wynants, L. et al. BMJ 369, m1328 (2020).
- Gupta, R. K. et al. Preprint at https://doi. org/10.1101/2020.07.24.20149815 (2020). · DOI
- Király, F. J., Mateen, B. & Sonabend, R. Preprint at https://arxiv. org/abs/1812.07519 (2018).
- Vollmer, S. et al. BMJ 368, l6927 (2020).
- Lipton, Z. C. & Steinhardt, J. Preprint at https://arxiv.org/ abs/1807.03341 (2018). · arXiv
- Wagstaff, K. L. in Proc. 29th Int. Conf. Machine Learning 529-536 (ICML, 2012).
- Authors
- Mateen, B. A., Liley, J., Denniston, A. K., Holmes, C. C., & Vollmer, S. J.
- Affiliations
- The Alan Turing Institute (GB)
- University Hospitals Birmingham NHS Foundation Trust (GB)
- University of Oxford (GB)
- University of Warwick (GB)
- Corresponding author
- Vollmer, Sebastian J.
- Venue
- Nature Machine Intelligence
- Published
- 2020-10-02
- Volume
- 2
- Issue
- 10
- Pages
- 554-556
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W3091673453
- Cited by
- 61
- References
- 14
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 13 · 2023: 12 · 2022: 15 · 2021: 16
- FWCI
- 3.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (publishedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- clinical research, reporting guidelines, clinical prediction tool, reproducibility, clinical implementation, interdisciplinary collaboration
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