The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised objectives.
LLMs-Healthcare : Current Applications and Challenges of Large Language Models in various Medical Specialties
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abstract
We aim to present a comprehensive overview of the latest advancements in utilizing Large Language Models (LLMs) within the healthcare sector, emphasizing their transformative impact across various medical domains. LLMs have become pivotal in supporting healthcare, including physicians, healthcare providers, and patients. Our review provides insight into the applications of Large Language Models (LLMs) in healthcare, specifically focusing on diagnostic and treatment-related functionalities. We shed light on how LLMs are applied in cancer care, dermatology, dental care, neurodegenerative disorders, and mental health, highlighting their innovative contributions to medical diagnostics and patient care. Throughout our analysis, we explore the challenges and opportunities associated with integrating LLMs in healthcare, recognizing their potential across various medical specialties despite existing limitations. Additionally, we offer an overview of handling diverse data types within the medical field.
years
2026 3verdicts
UNVERDICTED 3representative citing papers
LLMs can infer individual domain knowledge from Slack logs with best MAE of 21.13% using Gemini 2.5 Flash, though performance varies by model and shows weak dependence on message volume.
All five tested LLMs deviated from US race-stratified disease distributions in synthetic case generation, while retrieval-based agentic workflows improved mean p-value by 0.0348, median p-value by 0.1166, and mean difference by 0.0949 for DeepSeek V3 in diagnosis ranking.
citing papers explorer
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Uncertainty-Aware Foundation Models for Clinical Data
The work introduces uncertainty-aware foundation models for clinical data by learning set-valued patient representations that enforce consistency across partial observations and integrate multimodal self-supervised objectives.
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Can AI Guess What You Know? Performance Comparison of Large Language Models for Human Domain Knowledge Estimation From Communication Logs
LLMs can infer individual domain knowledge from Slack logs with best MAE of 21.13% using Gemini 2.5 Flash, though performance varies by model and shows weak dependence on message volume.
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First, Do No Harm (With LLMs): Mitigating Racial Bias via Agentic Workflows
All five tested LLMs deviated from US race-stratified disease distributions in synthetic case generation, while retrieval-based agentic workflows improved mean p-value by 0.0348, median p-value by 0.1166, and mean difference by 0.0949 for DeepSeek V3 in diagnosis ranking.