Focus and Scope
One of the rapidly advancing frontiers in healthcare and medical innovation is the integration of artificial intelligence into clinical, diagnostic, and healthcare delivery systems. Artificial intelligence has demonstrated significant potential to improve disease detection, clinical decision-making, personalized treatment, and healthcare management. However, the increasing complexity of machine learning and deep learning models has raised critical concerns regarding transparency, interpretability, trust, fairness, accountability, and regulatory compliance. Advancing explainable artificial intelligence (XAI) in healthcare is therefore essential to ensure that AI-driven systems remain understandable, reliable, ethical, and acceptable for healthcare professionals, patients, and policymakers.
The journal is a forum for the exchange of research findings, analysis, information, and knowledge in areas that include, but are not limited to:
Explainable Artificial Intelligence for Clinical Decision Support Systems – The journal encourages research on explainable and interpretable AI models that support clinical decision-making processes. Topics include clinical prediction models, diagnostic support systems, treatment recommendation systems, risk assessment, prognostic analytics, physician-centered AI, human-AI collaboration, and methods that enhance transparency and trust in healthcare decision support.
Interpretable Machine Learning and Healthcare Analytics – The journal promotes research on machine learning, deep learning, data mining, and predictive analytics techniques that provide transparent and understandable outcomes for healthcare stakeholders. Topics include electronic health record analytics, disease prediction, patient monitoring, population health analysis, multimodal healthcare data integration, and explainability frameworks for healthcare data-driven systems.
Explainable AI for Medical Imaging and Biomedical Applications – The journal encourages research on transparent and interpretable AI technologies applied to medical imaging, biomedical engineering, and healthcare diagnostics. Topics include explainable computer vision, radiology, pathology, image classification and segmentation, biomedical signal analysis, disease detection systems, and AI-assisted diagnostic applications that provide clinically meaningful explanations.
Ethical, Trustworthy, and Responsible AI in Healthcare – The journal supports research addressing the ethical, social, legal, and governance aspects of healthcare AI. Topics include fairness and bias mitigation, accountability mechanisms, trustworthy AI frameworks, privacy-preserving AI, regulatory compliance, healthcare AI governance, patient trust, transparency standards, and responsible deployment of AI technologies in clinical and public health settings.
Personalized Medicine and Precision Healthcare through Explainable AI – The journal welcomes research on AI-driven personalized healthcare solutions that provide interpretable insights for individualized patient care. Topics include precision medicine, genomic data interpretation, personalized treatment recommendations, predictive healthcare systems, digital therapeutics, and patient-centered AI applications that improve healthcare outcomes while maintaining transparency and explainability.