Evidence map›Paper›PMID 35584620›Full record

ReviewCell reports. Medicine2022

Artificial intelligence in medicine: Overcoming or recapitulating structural challenges to improving patient care?

Alex John London

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Ethics of Autonomous AI Clinical Trials: Delphi Study.Journal of medical Internet research · 2026
    Article
  5. Article
  6. Five views on the foundations of data science.Patterns (New York, N.Y.) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Alex John LondonDepartment of Philosophy and Center for Ethics and Policy, Carnegie Mellon University, Pittsburgh, PA 15228, USA. Electronic address: ajlondon@andrew.cmu.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial IntelligenceMedicineHumansPatient Careartificial intelligencebiasbioethicsequityhealthcarelearning health systemsresearch ethicssocial determinants of healthsocial valuestructural injustice

Identifiers

PMID35584620
PMCPMC9133460

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.