AI and Explainable Intelligence for Medical Diagnosis: Emerging Trends and Future Directions

Dear Colleagues,

Artificial Intelligence (AI) is rapidly transforming medical diagnosis by enhancing accuracy, efficiency, and clinical decision-making across varying data modalities. Beyond traditional deep learning approaches, recent advances in multimodal learning, explainable intelligence, and foundation models are reshaping how diagnostic information is analysed and interpreted. These models integrate medical imaging, clinical records, laboratory data, and textual reports to provide more comprehensive and context-aware diagnostic insights.

Explainable AI (XAI) has become a key focus, providing interpretable model outputs, improving transparency, and helping clinicians to build trust in AI-assisted decisions. Generative AI is also widely used for data augmentation, privacy‑preserving synthetic data, and cross‑modality translation, addressing data scarcity and variability. Meanwhile, domain adaptation, hybrid models, and workflow automation continue to expand AI’s clinical applicability.

Despite these advances, challenges remain, including data privacy, model robustness, interpretability consistency, and seamless clinical integration. Overall, AI and explainable intelligence are evolving to develop reliable, interpretable, and clinically deployable systems to shape the next generation of precision diagnostics.

Dr. Olamilekan Shobayo
Guest Editor

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