Evidence map›Paper›PMID 41559289›Full record

ArticleJournal of general internal medicine2026

Elevating Management Reasoning to Preserve Professional Identity in the AI Era.

R Logan Jones, Adam Rodman, Andrew S Parsons

Abstract read
In one paragraph

Article in Journal of general internal medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Review
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

3 authors.

R Logan JonesOregon Health and Science University, Portland, OR, USA.ORCID 0000-0002-7836-933X
Adam RodmanCarl J. Shapiro Institute for Education and Research, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-8452-0692
Andrew S ParsonsDepartment of Medicine, Division of Hospital Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA. asp5c@uvahealth.org.ORCID 0000-0001-5631-9465

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

AI systems are rapidly approaching expert-level diagnostic reasoning; however, management reasoning -the art of translating diagnoses into personalized care-remains distinctly human. While both are forms of clinical judgment, they emphasize different skills: diagnosis is a categorization task that answers "what's wrong?"; management is a prioritization task that answers "what should we do?" Medicine has long privileged diagnostic virtuosity. We drill differential diagnosis in morning report but rarely teach with equal rigor how to navigate real-world trade-offs for patients who value independence over longevity. This imbalance leaves us vulnerable as AI masters pattern recognition. Management reasoning requires what AI cannot currently replicate: integrating human values, navigating system constraints, and building therapeutic relationships. These fundamentally human skills-not diagnostic prowess-should anchor our professional identity. To secure medicine's future, we argue for a deliberate reorientation of medical education and clinical culture. Making space to teach and showcase management reasoning is not just an educational priority; it is essential for defending clinician judgment, relational care, and public trust as AI reshapes medicine's social contract. The goal is not merely finding the "right" answer but preparing clinicians to find the best answer for each unique patient. This is how human care remains truly human.

Indexed as

Artificial IntelligenceClinical ReasoningClinical CompetenceHumans

Identifiers

PMID41559289
PMCPMC13241326

What OpenQuestion holds

Textmetadata
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.