Explainable artificial intelligence in medical research: A synopsis for clinical practitioners—Comprehensive XAI methodologies
Küçük Resim Yok
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Enhancing the interpretability and transparency of AI models for scientific and medical research is the goal of Explainable Artificial Intelligence (XAI). AI is being used more and more in clinical decision support systems, thus it's important to understand how these systems function and produce outcomes. In activities like finding complicated data patterns, tracking treatment progress, and Disease diagnosis, XAI helps AI models provide more dependable and accurate predictions. XAI facilitates improved patient care and fortifies the decision-making process in medical settings by enhancing the clarity of AI-generated information. Therefore, it is important to use XAI in medical research to increase reliability and make more accurate decisions. The structure that produces findings is known as a “black box.” With XAI, doctors can understand the decisions made by AI because it overcomes the “black box” problem and offers transparency. Medical doctors can make safer and more informed decisions by considering not only the results but also the processes that lead to them. Given that the goal of interest is to provide more accurate predictions and an explanation of their rationale, XAI is a vital tool for both medical research and patient care procedures. This section covers the problems and solutions related to the interpretability of AI and machine learning techniques. It also focuses on the use of XAI algorithms to assist doctors in medical fields. © 2025 Elsevier Inc. All rights reserved.
Açıklama
Anahtar Kelimeler
Disease diagnosis, Explainable artificial intelligence, Machine learning
Kaynak
Explainable AI in Healthcare Imaging for Medical Diagnoses: Digital Revolution of Artificial Intelligence
WoS Q Değeri
Scopus Q Değeri
N/A
