Artificial Intelligence in Clinical Medicine—Transforming Patient Care Through Innovation
Special Issue Editors
Interests: AI in medicine; medical image analysis; deep learning; diagnostic automation; computational biology
Interests: submodular function maximization; camera calibration; greedy algorithm; gaussian process; image selection
2. International Research Institute of Disaster Science, Tohoku University, Sendai 980-8577, Miyagi, Japan
Interests: dengue; malaria; leptospirosis; HIV; tuberculosis; inflammation; galectins; osteopontin
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Special Issue Information
Dear Colleagues,
This Special Issue of Reports highlights the growing impact of artificial intelligence (AI) across clinical medicine, with a focus on real-world case studies, translational research, and critical reviews. We welcome submissions that demonstrate AI’s role in improving diagnosis, treatment, and healthcare delivery, particularly in the following areas:
- AI in Diagnostics and Precision Medicine
- Medical Imaging and Pathology: AI-assisted radiology (CT, MRI, X-ray), digital pathology (WSI analysis), dermatology, and ophthalmology.
- Genomics and Multi-Omics AI:
- Clinical variant interpretation, polygenic risk prediction, and pharmacogenomics.
- Microbiome and Metabolomics AI: disease biomarker discovery, host–microbiome interactions.
- Rare and Complex Diseases: AI for early detection, differential diagnosis, and patient stratification.
- AI in Therapeutics and Personalized Medicine
- Precision Oncology: AI-driven tumor profiling (TMB, MSI), immunotherapy response prediction.
- Drug Discovery and Repurposing: deep learning for novel drug targets and combination therapies.
- Digital Therapeutics: AI-powered interventions in mental health, neurology, and chronic disease management.
- Clinical Implementation and Ethical Considerations
- NLP and Clinical Workflow: automated documentation (EHRs), real-time decision support.
- Bias, Fairness and Regulatory AI: mitigating disparities in AI models, FDA/CE compliance.
- Explainability and Clinician–AI Collaboration: ensuring trust and usability in real-world practice.
We particularly encourage case reports demonstrating AI’s impact on individual patient care (e.g., "AI-guided diagnosis of a rare genetic disorder") alongside validation studies and critical appraisals of AI failures in clinical settings. Submissions from clinician–AI researcher collaborations are highly valued.
Dr. Ryo Ozuru
Dr. Yuji Oyamada
Prof. Dr. Toshio Hattori
Guest Editors
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Keywords
- AI in diagnostics and precision medicine
- AI in therapeutics and personalized medicine
- clinical implementation and ethical considerations
- clinician–AI researcher collaborations
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