Explainable AI Models for Healthcare Diagnostics
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ID: 309120
2022
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Abstract
The integration of Artificial Intelligence (AI) into healthcare diagnostics has revolutionized disease detection, treatment recommendations, and patient monitoring. However, the “black-box” nature of AI models poses significant challenges to trust, interpretability, and clinical adoption. Explainable AI (XAI) models address these concerns by offering transparency in model decisions and highlighting feature relevance in medical predictions. This paper reviews the design and application of explainable AI models in healthcare, focusing on interpretable deep learning frameworks, model-agnostic methods, and ethical implications. Furthermore, it emphasizes how explainability bridges the gap between clinicians and AI systems, fostering confidence and accountability in medical diagnostics.
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| Authors | Usman Ali |
| Journal | International journal of advanced sciences and computing |
| Year | 2022 |
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| Keywords | Keywords not found |
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