Explainable AI: Developing Interpretable Deep Learning Models for Medical Diagnosis

Ruchi Thakur

doi:10.36948/ijfmr.2024.v06i04.25281

Explainable AI: Developing Interpretable Deep Learning Models for Medical Diagnosis

Author(s)	Ruchi Thakur
Country	India
Abstract	Artificial Intelligence (AI) and Deep Learning (DL) have demonstrated remarkable potential in enhancing medical diagnosis across various specialties. However, the inherent complexity and opacity of these models pose significant challenges in clinical adoption, particularly due to the critical nature of healthcare decisions. This research paper explores the development of interpretable deep learning models for medical diagnosis, focusing on the integration of Explainable AI (XAI) techniques to enhance transparency, accountability, and trust in AI-assisted medical decision-making. We investigate various XAI methodologies, their application in different medical domains, and their impact on diagnostic accuracy and clinical interpretability. Through a comprehensive analysis of case studies, we demonstrate how explainable models can not only maintain high diagnostic performance but also provide valuable insights into their decision-making processes, potentially revolutionizing the synergy between AI and human expertise in healthcare.
Keywords	Explainable AI; Deep Learning; Medical Diagnosis; Interpretability; Healthcare; Artificial Intelligence
Field	Computer Applications
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-07-26
Cite This	Explainable AI: Developing Interpretable Deep Learning Models for Medical Diagnosis - Ruchi Thakur - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.25281
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.25281
Short DOI	https://doi.org/gt5hh2

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International Journal For Multidisciplinary Research

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Explainable AI: Developing Interpretable Deep Learning Models for Medical Diagnosis

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