The field of healthcare is witnessing significant developments with the integration of large language models (LLMs) in various applications. Recent studies have showcased the potential of LLMs in enhancing breast cancer prediction, improving depression diagnosis, and facilitating automated anamnesis. The use of LLMs in medical error detection and correction has also shown promise, with retrieval-augmented dynamic prompting outperforming traditional prompting strategies. Furthermore, multi-modal LLMs have demonstrated improved performance in depression detection by integrating visual understanding into audio language models. Noteworthy papers include 'Enhancing Breast Cancer Prediction with LLM-Inferred Confounders', which leveraged LLMs to infer confounding diseases and improve breast cancer prediction, and 'It Hears, It Sees too: Multi-Modal LLM for Depression Detection', which proposed a novel multi-modal LLM framework for depression detection. These advancements highlight the potential of LLMs in transforming healthcare applications and improving patient outcomes.
Advancements in Large Language Models for Healthcare Applications
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ChineseErrorCorrector3-4B: State-of-the-Art Chinese Spelling and Grammar Corrector
Enhancing Breast Cancer Prediction with LLM-Inferred Confounders
The Potential and Limitations of Vision-Language Models for Human Motion Understanding: A Case Study in Data-Driven Stroke Rehabilitation
APRIL: Annotations for Policy evaluation with Reliable Inference from LLMs
Leveraging Evidence-Guided LLMs to Enhance Trustworthy Depression Diagnosis
The Alignment Paradox of Medical Large Language Models in Infertility Care: Decoupling Algorithmic Improvement from Clinical Decision-making Quality
Clinician-Directed Large Language Model Software Generation for Therapeutic Interventions in Physical Rehabilitation
Towards Robust and Fair Next Visit Diagnosis Prediction under Noisy Clinical Notes with Large Language Models
The Locally Deployable Virtual Doctor: LLM Based Human Interface for Automated Anamnesis and Database Conversion
A Recommender System Based on Binary Matrix Representations for Cognitive Disorders
Profile Generators: A Link between the Narrative and the Binary Matrix Representation
Using Wearable Devices to Improve Chronic PainTreatment among Patients with Opioid Use Disorder
KOM: A Multi-Agent Artificial Intelligence System for Precision Management of Knee Osteoarthritis (KOA)
Large Language Model Aided Birt-Hogg-Dube Syndrome Diagnosis with Multimodal Retrieval-Augmented Generation
A Systematic Analysis of Large Language Models with RAG-enabled Dynamic Prompting for Medical Error Detection and Correction
It Hears, It Sees too: Multi-Modal LLM for Depression Detection By Integrating Visual Understanding into Audio Language Models
Editing with AI: How Doctors Refine LLM-Generated Answers to Patient Queries
"When Data is Scarce, Prompt Smarter"... Approaches to Grammatical Error Correction in Low-Resource Settings
Cognitive bias in LLM reasoning compromises interpretation of clinical oncology notes
LungNoduleAgent: A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules