Clinical Application of Large Language Models for Breast Conditions: A Systematic Review

Abstract

Background; The application of artificial intelligence (AI) like Large Language Models (LLM) into the healthcare system has been a frequently discussed topic in recent years. Materials and Methods; We conducted a systemic review on primary studies about the applications of LLM in breast conditions. The studies are then categorized into their respective domains, namely diagnosis, management recommendations and communication for patients. Results; The diagnostic accuracy ranged from 74.3% to 99.6% across different investigation modalities. The concordance of management recommendations ranged from 50% to 70% while the prognostic evaluation of breast cancer patients of distant recurrence showed an accuracy of 75% to 88%. In regards to patient communication, it is revealed that 18-30% of the references used by the LLM were irrelevant. Conclusion; This study highlights the potential benefits of LLM in strengthening patient communication, diagnose and management of patients with breast conditions. With standardized protocol and guideline to minimize potential risks, LLM can be a valuable tool to support future clinicians in the field of breast management.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The study used ONLY openly available human data that were originally located at Pubmed, SCOPUS, and Google Scholar databases

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Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Data Availability

All data produced in the present work are contained in the manuscript

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