Cited by 1,447Artificial Intelligence in HealthcareClinical Reasoning and Diagnostic SkillsBayesian Modeling and Causal Inference

A model of inexact reasoning in medicine

Shortliffe, Edward H., Buchanan, Bruce G.·1975·Mathematical Biosciences

Integrity signals

Registry

Crossref · 10.1016/0025-5564(75)90047-4

Cites

38 of 38 references verified0 citation errors · 0 not checkable

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  • Making AI systems explainable has been a focus of research since at least 1975 and has gained more attention recently given the increasingly pervasive distribution of AI systems.

    “It has been a focus of research since at least 1975 (Shortliffe and Buchanan 1975). And it has gained more attention recently (Thelisson et al. 2017; Wachter et al. 2016) given the increasingly pervasive distribution of AI systems.”

    From How to Design AI for Social Good: Seven Essential Factors (2020) · cites this paper as Shortliffe and Buchanan 1975

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Authors
Shortliffe, E. H., & Buchanan, B. G.
Affiliations
Stanford Medicine (US)
Venue
Mathematical Biosciences
Publisher
Elsevier BV
Published
1975-04
Volume
23
Issue
3-4
Pages
351-379
ISSN
0025-5564 (ISSN-L)
OpenAlex
W188378287
Cited by
1,447
References
38
Citations by year
2026: 45 · 2025: 41 · 2024: 41 · 2023: 77 · 2022: 48 · 2021: 62 · 2020: 56 · 2019: 37 · 2018: 37 · 2017: 23 · 2016: 32 · 2015: 38 · 2014: 30 · 2013: 42 · 2012: 25
FWCI
71.21
Type
journal article
Language
en
Open access
closed
Locations
Mathematical Biosciences (publishedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
Keywords
inexact reasoning, clinical decision-making, expert reasoning, MYCIN

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.

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