Conversational Disease Diagnosis via External Planner-Controlled Large Language Models
This study introduces an innovative approach using external planners augmented with large language models (LLMs) to develop a medical task-oriented dialogue system for conversational disease diagnosis. The system comprises a policy module for information gathering, and an LLM-based module for natural language understanding and generation, addressing the limitations of previous AI systems in these areas.
Enhancing Medical Instruction-Following Ability of Large Language Models through Diverse Machine-Generated Dataset
Tuning large language models with a diverse, machine-generated medical instruction-response dataset, MedInstruct-52k, can significantly boost their performance on medical applications while also improving their generalizability.
LLM-based Personalized Medical Assistant with Dual-Process Enhanced Memory and Parameter-Efficient Fine-Tuning
A novel computational bionic memory mechanism, equipped with a parameter-efficient fine-tuning schema, is proposed to personalize medical assistants and enhance their response quality by catering to user-specific needs.
Meerkat-7B: An Open-Source Medical AI System with Enhanced Reasoning Skills from Textbooks
Meerkat-7B, a novel 7-billion parameter open-source medical AI system, achieves state-of-the-art performance on medical benchmarks by leveraging chain-of-thought reasoning data synthesized from medical textbooks.
Addressing Fairness in Vision-Language Models for Equitable Medical Diagnosis
Significant biases exist in widely-used vision-language models, with Asian, Male, Non-Hispanic, and Spanish being the preferred subgroups across the protected attributes of race, gender, ethnicity, and language, respectively. FairCLIP, an optimal transport-based approach, achieves a favorable trade-off between performance and fairness by reducing the Sinkhorn distance between the overall sample distribution and the distributions corresponding to each demographic group.
Improving Cancer Diagnosis Accuracy through Bayesian Deep Learning Approaches
Combining the strengths of Bayesian Networks and Deep Learning models can improve the accuracy and reliability of cancer imaging diagnosis.
ArgMed-Agents: Enhancing Clinical Decision Reasoning with Argumentation Schemes
ArgMed-Agents improves clinical decision reasoning accuracy and explainability through argumentation schemes.
Choroid Neoplasias Diagnosis Model with Multimodal Data
Interpretable AI model for diagnosing choroid neoplasias with high accuracy and improved diagnostic performance for junior doctors.
Apollo: Lightweight Multilingual Medical LLMs for Democratizing Medical AI
개발된 Apollo은 61억 인구를 대상으로 하는 최첨단 다국어 의료 LLMs를 제공합니다.
SERVAL: Synergy Learning for Zero-shot Medical Prediction
SERVAL is an unsupervised learning pipeline that enhances zero-shot medical prediction by leveraging the synergy between large language models (LLMs) and vertical models.
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