Evidence map›Paper›PMID 42622965›Full record

ReviewPituitary2026

Artificial intelligence in pituitary medicine: what should an endocrinologist know?

Damla N Costa, Rozalina G McCoy, Debraj Mukherjee, Roberto Salvatori

Abstract readReview
PubMed Publisher
In one paragraph

Review in Pituitary, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Damla N CostaDepartment of Medicine, University of Maryland Midtown Campus, Baltimore, USA.ORCID http://orcid.org/0000-0002-1617-5134
Rozalina G McCoyDivision of Endocrinology, Diabetes, & Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, USA.ORCID http://orcid.org/0000-0002-2289-3183
Debraj MukherjeeDepartment of Neurosurgery and Pituitary Center, Johns Hopkins University School of Medicine, Baltimore, USA.ORCID http://orcid.org/0000-0002-5403-8237
Roberto SalvatoriDivision of Endocrinology, Diabetes and Metabolism and Pituitary Center, Johns Hopkins University School of Medicine, 1830 East Monument Street #333, Baltimore, MD, 21287, USA. salvator@jhmi.edu.ORCID http://orcid.org/0000-0001-6495-2244

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pituitary disorders often include complex radiology imaging, rare clinical presentations, and need for multidisciplinary decision-making and prolonged follow-up, all features that make them a natural target for artificial intelligence (AI). The resulting literature is expanding rapidly, but carries an unfamiliar vocabulary and set of methods, which can leave clinicians without a clear entry point. This short perspective offers such an entry point, succinctly outlining how AI learns from data, how a clinical AI application moves from question to deployment, providing examples, and addressing how to read the field critically.

Indexed as

Artificial IntelligenceEndocrinologistsPituitary DiseasesPituitary GlandEndocrinologyHumansMachine LearningArtificial intelligenceDeep learningEndocrinologyMachine learningPituitary medicine

Identifiers

PMID42622965

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.