<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Omar M</submitter><funding>National Cancer Institute</funding><funding>NCI NIH HHS</funding><funding>US Department of Defense</funding><pagination>e595-e600</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC11299190</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>6(8)</volume><pubmed_abstract>The rapid evolution of generative artificial intelligence (AI) models including OpenAI's ChatGPT signals a promising era for medical research. In this Viewpoint, we explore the integration and challenges of large language models (LLMs) in digital pathology, a rapidly evolving domain demanding intricate contextual understanding. The restricted domain-specific efficiency of LLMs necessitates the advent of tailored AI tools, as illustrated by advancements seen in the last few years including FrugalGPT and BioBERT. Our initiative in digital pathology emphasises the potential of domain-specific AI tools, where a curated literature database coupled with a user-interactive web application facilitates precise, referenced information retrieval. Motivated by the success of this initiative, we discus</pubmed_abstract><journal>The Lancet. Digital health</journal><pubmed_title>ChatGPT for digital pathology research.</pubmed_title><pmcid>PMC11299190</pmcid><funding_grant_id>U54 CA273956</funding_grant_id><funding_grant_id>P50 CA211024</funding_grant_id><funding_grant_id>P01 CA265768</funding_grant_id><pubmed_authors>Omar M</pubmed_authors><pubmed_authors>Ullanat V</pubmed_authors><pubmed_authors>Umeton R</pubmed_authors><pubmed_authors>Loda M</pubmed_authors><pubmed_authors>Marchionni L</pubmed_authors></additional><is_claimable>false</is_claimable><name>ChatGPT for digital pathology research.</name><description>The rapid evolution of generative artificial intelligence (AI) models including OpenAI's ChatGPT signals a promising era for medical research. In this Viewpoint, we explore the integration and challenges of large language models (LLMs) in digital pathology, a rapidly evolving domain demanding intricate contextual understanding. The restricted domain-specific efficiency of LLMs necessitates the advent of tailored AI tools, as illustrated by advancements seen in the last few years including FrugalGPT and BioBERT. Our initiative in digital pathology emphasises the potential of domain-specific AI tools, where a curated literature database coupled with a user-interactive web application facilitates precise, referenced information retrieval. Motivated by the success of this initiative, we discus</description><dates><release>2024-01-01T00:00:00Z</release><publication>2024 Aug</publication><modification>2026-05-04T06:37:01.331Z</modification><creation>2026-04-22T03:09:49.785Z</creation></dates><accession>S-EPMC11299190</accession><cross_references><pubmed>38987117</pubmed><doi>10.1016/S2589-7500(24)00114-6</doi></cross_references></HashMap>