Evidence map›Paper›PMID 42455485›Full record

ArticleAnnals of surgical oncology2026

Artificial Intelligence-Generated Electronic Medical Record Summarization in Breast Surgical Oncology.

Ko Un Park, Bergen K Sather, Anika Shah, Christina A Minami, Matthew Butler, Lara Butler, Jordan A Love, Kathleen McLean, Adam Dunn, Elizabeth A Mittendorf and 1 more

Abstract read
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In one paragraph

Article in Annals of surgical oncology, 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

11 authors.

Ko Un Park *Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA. kpark16@bwh.harvard.edu.
Bergen K Sather *Ariadne Labs, Brigham and Women's Hospital, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Anika ShahDivision of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Christina A MinamiDivision of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Matthew ButlerBrown University Health, Providence, RI, USA.
Lara ButlerDivision of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Jordan A LoveDivision of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Kathleen McLeanDivision of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Adam DunnDivision of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Elizabeth A MittendorfDivision of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.
Tari A KingDivision of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundReviewing pathology, imaging, and consultation documents in oncology can be time-consuming, particularly when records originate from external facilities in different file formats. This study aimed to evaluate the impact of a Retrieval-Augmented Generation (RAG)-enabled GPT-4o summarization agent on clinical workflows and quality of outside-record summaries in breast surgical oncology.

methodsInitial performance evaluation of a GPT-4o/RAG agent to generate summaries of oncologic reports in 50 charts followed by a prospective pilot test of sequential cases, with each AI summary evaluated using a modified Provider Documentation Summarization Quality Instrument (PDSQI-9; 1-5 Likert scale), including dichotomized ratings (low [1-3], high [4, 5]), binomial testing, frequency and type of user-reported errors, clinician-coded error criticality (treatment-impacting vs noncritical). Pre- and post-use survey of documentation burden (NASA TLX) and user experience was performed.

resultsAmong 62 cases, AI-generated summaries were rated high for accuracy, usefulness, succinctness, and source citation. Thoroughness without omission was rated low in 28 (45%) summaries. Errors were noted in 25 (40%) surveys, with 13 (52%) classified as critical (treatment-impacting). The most common error type involved imaging, reported in 17 (68%) cases. For perceived time savings, the median response was neutral, but qualitative feedback described the tool as helpful for straightforward cases and as reducing typing burden but requiring workflow adjustment and improvements for complex cases.

conclusionsAlthough users rated RAG-enabled GPT-4o agent-generated documentation summaries favorably on several quality domains, they frequently lacked thoroughness and occasionally contained treatment-relevant errors. Human review and further iteration of the technology remain necessary before implementation.

Indexed as

Artificial IntelligenceBreast NeoplasmsDocumentationElectronic Health RecordsSurgical OncologyFemaleGenerative Artificial IntelligenceHumansPilot ProjectsPrognosisProspective StudiesArtificial intelligenceBreast surgical oncologyLarge language modelMedical record summarizationPDSQI-9

Identifiers

PMID42455485
PMCPMC13585955

What Socratic holds

Textmetadata
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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 Socratic graph.