AI-Driven Diagnostic and Medical Case Reports (ISSN ) AI-Driven Diagnostic and Medical Case Reports: An International Open-Access Journal is a peer-reviewed, international journal dedicated to publishing high-quality medical case reports and case series that demonstrate the real-world application, evaluation, and implications of artificial intelligence (AI) and machine learning technologies in clinical diagnosis and patient care. The journal aims to bridge the gap between technological innovation and clinical practice by highlighting how AI-driven diagnostic tools are developed, validated, implemented, and interpreted in diverse healthcare settings. Emphasis is placed on clinical relevance, methodological transparency, patient safety, and ethical responsibility, ensuring that published work contributes meaningfully to evidence-based medical practice. The journal welcomes original medical case reports, short case series, technical notes, and clinical insights related to AI-assisted diagnostics across all medical specialties, including but not limited to radiology, pathology, cardiology, oncology, neurology, ophthalmology, dermatology, internal medicine, surgery, and emergency medicine. The scope of the journal includes AI-assisted diagnostic case reports and real-world clinical applications, clinical validation and performance assessment of AI diagnostic systems, case reports highlighting diagnostic success, failure, bias, or unintended consequences, and the integration of AI technologies into clinical workflows and decision-support systems. It also covers explainability and human–AI interaction in diagnostic practice, ethical, legal, and regulatory considerations, patient safety, accountability, and governance, as well as reports of rare, complex, or challenging cases and negative or inconclusive findings that offer important clinical lessons. The journal adheres to internationally recognized standards for ethical publishing, requiring appropriate patient consent, ethics approval where applicable, and full disclosure of conflicts of interest, while encouraging transparent reporting of datasets, algorithms, and validation methods. Through open-access publication, the journal aims to support clinicians, researchers, and policymakers in understanding both the potential and limitations of artificial intelligence in medical diagnostics.