Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?
Open accessAbstract
There is considerable enthusiasm about the prospect that artificial intelligence (AI) will help to improve the safety and efficacy of health services and the efficiency of health systems. To realize this potential, however, AI systems will have to overcome structural problems in the culture and practice of medicine and the organization of health systems that impact the data from which AI models are built, the environments into which they will be deployed, and the practices and incentives that structure their development. This perspective elaborates on some of these structural challenges and provides recommendations to address potential shortcomings.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
EHR data are often a complex product of biological, socio-economic conditions as well as prior practices of providers and health systems.
“For example, EHR data are often a complex product of biological, socio-economic conditions as well as prior practices of providers and health systems [54].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [54]
Some data sources used for training ML models, such as EHRs collected for administrative purposes, might not be gathered with the necessary frequency, granularity, or bandwidth that align with information needs of science and learning, and may present challenges in generating accurate and reliable algorithms.
“Some data sources used for training ML models, such as EHRs collected for administrative purposes, might not be gathered with the necessary frequency, granularity, or bandwidth that align with information needs of science and learning, and may therefore present challenges in generating accurate and reliable algorithms [54].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [54]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 51 · Cited by 76
Research works printed but not linked: 4
- [1]London A.J. For the Common Good: Philosophical Foundations of Research Ethics 2021 Oxford University Press
- [30]Pearl J. Causality 2009 Cambridge University Press
- [42]Nestor B. McDermott M.B. Boag W. Berner G. Naumann T. Hughes M.C. Goldenberg A. Ghassemi M. Feature robustness in non-stationary health records: caveats to deployable model performance in common clinical machine learning tasks Machine Learning for Healthcare Conference, PMLR 2019 381 405
- [50]Strickland E. How IBM Watson overpromised and underdelivered on AI health care-IEEE spectrum. IEEE spectrum: technology, engineering, and science news https://spectrum.ieee.org/biomedical/diagnostics/how-ibm-watson-overpromised-and-underdelivered-on-ai-health-care 2019
- Authors
- London, A. J.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- London, Alex John
- ORCID
- London, Alex John
- Venue
- Cell Reports Medicine
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 3
- Issue
- 5
- Pages
- 100622
- ISSN
- 2666-3791 (ISSN-L)
- PMC
- PMC9133460
- OpenAlex
- W4280519102
- Cited by
- 76
- References
- 51
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 23 · 2023: 22 · 2022: 9
- FWCI
- 1.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports Medicine (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PhilPapers (PhilPapers Foundation) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence in healthcare, structural challenges, health system organization, health care incentives, quality of care, health system efficiency
- MeSH terms
- Artificial Intelligence, Patient Care, Humans, Medicine
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
