Journals

  • Journal of Clinical Pharmacotherapy and Medicines Use

    Journal of Clinical Pharmacotherapy and Medicines Use (JCPMU) is a multidisciplinary scholarly journal dedicated to advancing the safe, effective, appropriate, and evidence-based use of medicines in clinical practice.

    The journal publishes research in clinical pharmacotherapy, medication safety, medicines use and drug utilization, pharmacoepidemiology, pharmacovigilance, therapeutic outcomes, prescribing quality, real-world evidence, adherence, deprescribing, medication reconciliation, medicines optimization, and related areas.

    JCPMU welcomes quantitative, qualitative, mixed-methods, and evidence-synthesis research from medicine, clinical pharmacy, clinical pharmacology, nursing, dentistry, pharmacoepidemiology, and related health disciplines when medicines or therapeutic decision-making are central to the work.

    Carefully selected medication-focused case reports and case series are also considered where they provide a substantive educational contribution to pharmacotherapy or medication safety.

  • Clinical AI in Practice

    Clinical AI in Practice

    Evidence, safety and outcomes for real-world clinical AI

    About the Journal

    Clinical AI in Practice is a peer-reviewed, open-access journal dedicated to the rigorous evaluation, responsible implementation and effective governance of artificial intelligence in patient care and healthcare delivery.

    The journal addresses the gap between technical model performance and meaningful clinical value. It publishes original research, clinical validation studies, trials, implementation studies, systematic reviews, methods articles, safety analyses, policy research and expert perspectives relevant to the development and use of AI in real-world clinical settings.

    The journal’s scope includes clinical decision support; diagnostic and prognostic AI; treatment selection and therapeutic monitoring; medical imaging; computational pathology; clinical natural language processing; generative and multimodal AI; large language models; clinical AI agents; autonomous and semi-autonomous systems; human–AI interaction; workflow integration; external and prospective validation; patient safety; algorithmic bias; health equity; explainability; regulatory science; clinical-AI governance; post-deployment monitoring; model drift; health economics; and patient, clinician and health-system outcomes.

    Clinical AI in Practice prioritises scientifically rigorous, transparent and clinically relevant research that defines the intended use of an AI system, evaluates its benefits and limitations, and considers its effects on patients, healthcare professionals and clinical services. The journal welcomes positive, negative and replication studies that strengthen the evidence base for safe, effective and equitable clinical AI.

    The journal serves clinicians, AI researchers, health informaticians, data scientists, biomedical engineers, implementation scientists, healthcare leaders, patient-safety specialists, policymakers and regulators working at the intersection of artificial intelligence and clinical practice.