Artificial Intelligence and Machine Learning in Clinical Practice

Dear Colleagues,

The increasing adoption of artificial intelligence (AI) and machine learning (ML) in clinical practice marks a transformative shift in modern healthcare, promising enhanced diagnostic precision, treatment personalization, and operational efficiency. Despite growing enthusiasm, real-world integration remains challenged by issues such as clinical validation, interpretability, interoperability, ethical implications, and data bias. This Special Issue aims to address these pressing challenges by showcasing cutting-edge research that bridges AI/ML innovations with practical, patient-centered clinical applications.

The scope of this Special Issue includes, but is not limited to, the following:

• AI-driven diagnostic and prognostic systems.
• ML-enhanced decision support tools.
• Clinical risk prediction models.
• Automation in radiology and pathology.
• Real-time monitoring using wearable devices.
• Explainable AI in medical settings.

Dr. Theodosis Kalamatianos
Dr. Ioannis Kakkos
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

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