Computational Perspectives on Artificial Intelligence Drive in Medical Decision-Making
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "D2: Operations Research and Fuzzy Decision Making".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 66
Special Issue Editors
Interests: applied and computational statistics; molecular modeling; genetic analysis; statistical modeling in medicine; integrated health informatics system; medical diagnostic research; statistical inference; medical imaging analysis; assisted decision systems; research ethics; social media and health information; evidence-based medicine
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; intelligent systems; machine learning; medical informatics; statistics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) in healthcare holds significant potential for enhancing patient outcomes, improving operational efficiency, and refining clinical decision-making processes. AI is poised to revolutionize healthcare by increasing the accuracy and precision of diagnostics and screenings, predicting patient outcomes, optimizing treatment and monitoring strategies, streamlining workflows, and reducing healthcare costs, ultimately enhancing overall patient care delivery. Computational techniques, including machine learning, deep learning, and natural language processing, are particularly applicable in automating and improving decision-making processes in medicine. However, challenges related to interpretability, accuracy, precision, bias, and ethical considerations remain pertinent. To address these challenges and further explore AI's potential in healthcare, we invite authors to submit contributions on the following themes:
- AI-driven clinical decision support systems;
- Machine learning models for medical diagnosis and prognosis;
- Explainable AI (XAI) in healthcare decision-making;
- Deep learning applications in medical imaging and radiology;
- Data-driven predictive modelling for patient outcomes;
- Natural language processing in medical records and diagnostics;
- Bias, fairness, and ethics in AI-driven medical decisions;
- Federated learning and privacy-preserving AI in healthcare;
- Integration of AI with electronic health records (EHR);
- AI tools in continuing medical education and training;
- AI and patient-centred healthcare;
- AI ethical and operational challenges;
- AI-based antibiotic stewardship;
- AI healthcare management;
- Digital and virtual twins in healthcare;
- Digital and virtual twins in professional education.
The potential of AI tools for improving clinical operations, enhancing patient care, leveraging cost efficiencies, and facilitating rapid response mechanisms within the healthcare system is substantial. A non-exhaustive list of application-based areas of interest for this Special Issue includes computer science, biomedical sciences, engineering, business and economics, finance, and economic and social sciences.
Prof. Dr. Sorana D. Bolboacă
Dr. Sebastian Stefaniga
Guest Editors
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Keywords
- AI-driven healthcare systems
- machine learning for medical diagnosis and prognosis
- ethics and explainability in AI healthcare
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