A fairer way forward for AI in health care
Open accessThe 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?
An algorithm used to predict hospital length of stay to consult case managers for patients with shorter predicted length of stay was abandoned before deployment.
“A similar example is an algorithm used to predict hospital length of stay to consult case managers for patients with shorter predicted length of stay to maximize throughput. 16 The algorithm (which was abandoned before deployment) learnt that patients from less affluent zip codes are likely to have longer hospital stays and may not benefit from case management facilitation of early discharge.”
From AI pitfalls and what not to do: mitigating bias in AI (2023) · cites this paper as [16]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Lee, P., LeSaux, M. A., Siegel, R. B., Goyal, M. K., Chen, C., Ma, Y., & Meltzer, A. C. (2019) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2019.06.014 · Cited by 318 · see citing works
Black, E., & RICHMOND, R. L. (2019) · Globalization and Health · doi:10.1186/s12992-018-0446-6 · Cited by 207 · see citing works
Montgomery, K. C., Chester, J., & Kopp, K. (2018) · Journal of Information Policy · doi:10.5325/jinfopoli.8.2018.0034 · Cited by 50 · see citing works
The Ethics of AI in Biomedical Research, Patient Care and Public Health
Blasimme, A., & Vayena, E. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3368756 · Cited by 48 · see citing works
Policies designed for drugs won’t work for AI
Smallman, M. (2019) · Nature · doi:10.1038/d41586-019-00737-2 · Cited by 23 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Ethical Issues of Artificial Intelligence in Medicine and Healthcare
Farhud, D. D., & Zokaei, S. (2021) · Iranian Journal of Public Health · doi:10.18502/ijph.v50i11.7600 · Cited by 416 · see citing works
AI pitfalls and what not to do: mitigating bias in AI
Gichoya, J. W., Thomas, K., Celi, L. A., Safdar, N., Banerjee, I., Banja, J. D., Seyyed-Kalantari, L., Trivedi, H., & Purkayastha, S. (2023) · The British Journal of Radiology · doi:10.1259/bjr.20230023 · Cited by 277 · see citing works
“16 The algorithm (which was abandoned before deployment) learnt that patients from less affluent zip codes are likely to have longer hospital stays and may not benefit from case management facilitation of early discharge.”
An empirical characterization of fair machine learning for clinical risk prediction
Pfohl, S. R., Foryciarz, A., & Shah, N. H. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103621 · Cited by 164 · see citing works
Dankwa-Mullan, I., Scheufele, E. L., Matheny, M. E., Quintana, Y., Chapman, W. W., Jackson, G., & South, B. R. (2021) · Journal of Health Care for the Poor and Underserved · doi:10.1353/hpu.2021.0065 · Cited by 108 · see citing works
The case for algorithmic stewardship for artificial intelligence and machine learning technologies
Eaneff, S., Obermeyer, Z., & Butte, A. J. (2020) · JAMA · doi:10.1001/jama.2020.9371 · Cited by 101 · see citing works
Governing AI‐Driven Health Research: Are IRBs Up to the Task?
Friesen, P., Douglas‐Jones, R., Marks, M., Pierce, R., Fletcher, K., Mishra, A., Lorimer, J., Véliz, C., Hallowell, N., Graham, M., et al. (2021) · Ethics & Human Research · doi:10.1002/eahr.500085 · Cited by 50 · see citing works
- Authors
- Nordling, L.
- Corresponding author
- Nordling, Linda
- Venue
- Nature
- Published
- 2019-09-25
- Volume
- 573
- Issue
- 7775
- Pages
- S103-S105
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2976876609
- Cited by
- 90
- References
- 5
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 18 · 2023: 20 · 2022: 5 · 2021: 22 · 2020: 12
- FWCI
- 3.15
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (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, health care inequality, AI implementation, algorithmic fairness, health equity
- MeSH terms
- Algorithms, Artificial Intelligence, Chicago, Delivery of Health Care, Hospitals, Humans, Healthcare Disparities
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
