Evidence map›Paper›PMID 38504034›Full record

SynthesisJournal of cancer research and clinical oncology2024

Utilizing large language models in breast cancer management: systematic review.

Vera Sorin, Benjamin S Glicksberg, Yaara Artsi, Yiftach Barash, Eli Konen, Girish N Nadkarni, Eyal Klang

Abstract readSystematic Review
In one paragraph

Synthesis in Journal of cancer research and clinical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing 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

25 citing papers in PubMed.

  1. Article
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  3. Article
  4. Article
  5. Article
  6. Review
  7. The Role of Artificial Intelligence in Theranostics.Journal of nuclear medicine technology · 2025
    Review
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Observational
  17. Review
  18. Review
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  20. Article
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

7 authors.

Vera SorinDepartment of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel. verasrn@gmail.com.
Benjamin S GlicksbergDivision of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Yaara ArtsiAzrieli Faculty of Medicine, Bar-Ilan University, Zefat, Israel.
Yiftach BarashDepartment of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel.
Eli KonenDepartment of Diagnostic Imaging, Chaim Sheba Medical Center, Affiliated to the Sackler School of Medicine, Tel-Aviv University, Emek Haela St. 1, 52621, Ramat Gan, Israel.
Girish N NadkarniDivision of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Eyal KlangDivision of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

Funding

COVID and Translational Science supercomputer (CATS)S10OD030463 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2021 to 2021
$2.0M
Big Omics Data Engine 2 SupercomputerS10OD026880 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2019 to 2019
$2.0M
NIH HHS S10 OD026880NIH HHS S10 OD030463
6 · The paper itself

Abstract

purposeDespite advanced technologies in breast cancer management, challenges remain in efficiently interpreting vast clinical data for patient-specific insights. We reviewed the literature on how large language models (LLMs) such as ChatGPT might offer solutions in this field.

methodsWe searched MEDLINE for relevant studies published before December 22, 2023. Keywords included: "large language models", "LLM", "GPT", "ChatGPT", "OpenAI", and "breast". The risk bias was evaluated using the QUADAS-2 tool.

resultsSix studies evaluating either ChatGPT-3.5 or GPT-4, met our inclusion criteria. They explored clinical notes analysis, guideline-based question-answering, and patient management recommendations. Accuracy varied between studies, ranging from 50 to 98%. Higher accuracy was seen in structured tasks like information retrieval. Half of the studies used real patient data, adding practical clinical value. Challenges included inconsistent accuracy, dependency on the way questions are posed (prompt-dependency), and in some cases, missing critical clinical information.

conclusionLLMs hold potential in breast cancer care, especially in textual information extraction and guideline-driven clinical question-answering. Yet, their inconsistent accuracy underscores the need for careful validation of these models, and the importance of ongoing supervision.

Indexed as

Artificial IntelligenceBreast NeoplasmsFemaleHumansArtificial intelligenceBreast cancerGPTLarge language models

Identifiers

PMID38504034
PMCPMC10950983

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.