Explainable AI Models for Healthcare Diagnostics

Authors

  • Muhammad Ahsan Department of Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan
  • Sara Khan Department of Biomedical Engineering, COMSATS University Islamabad, Lahore Campus,
  • Usman Ali Pakistan Department of Artificial Intelligence, University of Engineering and Technology (UET), Lahore, Pakistan

Keywords:

Explainable AI, Healthcare diagnostics, Deep learning, Interpretability, Transparency, Model explainability, Clinical decision support, Ethical AI

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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Published

2022-06-30

Issue

Jan-Jun 2022

Section

Articles

License

All articles published in the International Journal of Advanced Sciences and Computing (IJASC) are open access and licensed under the Creative Commons Attribution (CC BY 4.0) license. Authors retain copyright, and users may read, share, reuse, and adapt the content with proper attribution.