Enhancing Trust in Healthcare through Interpretable AI Systems: A Systematic Review of Processes, Methods, and Challenges
This paper provides a comprehensive review of the processes, methods, and challenges associated with implementing interpretable machine learning (IML) and explainable artificial intelligence (XAI) within healthcare and medical domains, with the goal of improving communication and trust between AI systems and clinicians.
Designing AI-Based Systems to Support Clinicians' Diagnostic Reasoning: A Conceptual Framework for Complementarity
The core message of this article is to propose a conceptual framework for designing AI-based systems that support clinicians' diagnostic reasoning through feature importance, counterexample explanations, and similar-case explanations, rather than providing direct recommendations or explanations.
Assessment of GPT-4's Performance in a USMLE-based Case Study
Feedback influences relative confidence but doesn’t consistently increase or decrease it.
MCRAGE: Synthetic Healthcare Data for Fairness in Machine Learning Models
提案されたMCRAGEアプローチは、医療データの不均衡を解消し、公平性を向上させる革新的な方法です。
MCRAGE: Synthetic Healthcare Data for Fairness in Machine Learning Models
Addressing imbalanced healthcare datasets using MCRAGE to improve fairness in machine learning models.
FURM Framework for Evaluating AI Models in Healthcare Systems
AIモデルの公正性、有用性、信頼性を評価するためのFURMフレームワークが重要である。
Apollo: Lightweight Multilingual Medical LLMs for Democratizing Healthcare AI
医療AIの民主化を目指すための軽量多言語医療LLMの開発とその効果的な利用方法に焦点を当てる。
COMPRER: A Multimodal Multi-Objective Pretraining Framework for Enhanced Medical Image Representation
COMPRER introduces a novel multi-modal, multi-objective pretraining framework for enhanced medical image representation, diagnostic inferences, and prognosis of diseases.
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