Explainable Artificial Intelligence in Healthcare

Dr. Tri Wahyuningsih, S.T., M.T.I (Department of Informatics Management, AMIK YPAT Purwakarta, Indonesia)

Explainable Artificial Intelligence in Healthcare (XAIH) is an international, peer-reviewed scholarly journal published by LPPM AMIK YPAT Purwakarta that focuses on research and innovation in explainable artificial intelligence (XAI) and its applications in healthcare and medical systems. The journal publishes original research articles, review articles, and case studies addressing the development, evaluation, and application of transparent, interpretable, trustworthy, and responsible AI technologies in healthcare. Its scope covers a broad range of interdisciplinary fields, including explainable and interpretable machine learning and deep learning, clinical decision support systems, medical diagnosis and prediction, medical imaging, healthcare data analytics, personalized and precision medicine, human-centered AI, AI fairness and accountability, healthcare data privacy and security, as well as ethical, legal, regulatory, and governance aspects of AI in healthcare. XAIH welcomes theoretical, methodological, empirical, and applied research involving artificial intelligence, medicine, health informatics, medical informatics, data science, biomedical engineering, and healthcare policy. First published in 2026, XAIH is issued quarterly in February, May, August, and November and is published exclusively online under an open-access model, providing immediate and free access to all published articles. The journal aims to contribute to the advancement of reliable, transparent, and clinically meaningful AI technologies that support healthcare practice, clinical decision-making, patient safety, medical research, and the development of intelligent healthcare systems.

Topics covered include:

Explainable AI Models for Clinical Decision Support; Interpretable Machine Learning in Medical Diagnosis and Prediction; Transparent AI Systems for Medical Imaging and Healthcare Analytics; Ethical, Trustworthy, and Responsible AI in Healthcare; Human-Centered AI and Physician–AI Collaboration; AI Fairness, Bias Detection, and Accountability in Medical Systems; AI-Driven Personalized Medicine and Precision Healthcare; Regulatory, Legal, and Governance Frameworks for Explainable Healthcare AI.

XAIH aims to foster interdisciplinary collaboration among experts in artificial intelligence, medicine, health informatics, data science, bioengineering, and healthcare policy. The journal contributes to the advancement of transparent and reliable AI technologies that improve healthcare quality, patient safety, and clinical trust. Papers published in XAIH are grounded in rigorous theoretical, empirical, or applied research and are expected to clearly articulate their contributions to both scientific understanding and healthcare practice. Authors are encouraged to develop innovative explainability approaches while addressing broader implications such as ethics, privacy, accountability, and inclusivity in healthcare systems. In alignment with global priorities, XAIH welcomes research that supports sustainable healthcare innovation and contributes to the achievement of the United Nations 2030 Sustainable Development Goals (SDGs).

Subject Area and Category:

Explainable Artificial Intelligence in Healthcare focuses on the development, evaluation, and implementation of interpretable AI technologies in healthcare systems. The journal covers research on Explainable Machine Learning Models; Clinical Decision Support Systems; Transparent Medical Imaging Analytics; Ethical and Trustworthy AI in Healthcare; Personalized and Precision Medicine; AI Fairness and Accountability; and Healthcare Data Analytics and Governance. It also addresses interdisciplinary and real-world applications across hospitals, public health systems, digital health platforms, biomedical research, and healthcare policy environments.

Starting publishing date: 2026

Frequency: Quarterly (February, May, August, and November)

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