Advances in the Use of Machine Learning for Personalized Medicine
A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".
Deadline for manuscript submissions: 25 January 2026 | Viewed by 815
Special Issue Editor
2. Laboratory for Cardiometabolic Research, University of Split School of Medicine, Soltanska 2A, 21000 Split, Croatia
Interests: machine learning; deep learning; neural networks; medical information
Special Issue Information
Dear Colleagues,
The field of personalized medicine has been revolutionized by recent advancements in machine learning (ML), particularly with the emergence of large language models (LLMs) and other sophisticated AI technologies. These innovations are transforming healthcare decision-making, treatment planning, and patient care across various medical specialties. Historically, personalized medicine has evolved from a one-size-fits-all approach to increasingly tailored treatments based on individual patient characteristics. The integration of ML algorithms has accelerated this progression, enabling the analysis of vast and complex datasets to derive actionable insights for patient care. This Special Issue aims to explore cutting-edge ML applications in personalized medicine, with a particular focus on recent developments such as the use of LLMs in clinical decision support, AI-driven diagnostic tools, and predictive analytics for treatment optimization. We seek to highlight innovative research that demonstrates the potential of ML to enhance diagnostic accuracy, treatment efficacy, and patient outcomes. We welcome original research articles, comprehensive reviews, etc., that address topics including, but not limited to, the following:
- Applications of LLMs in clinical decision-making and shared decision-making processes;
- ML-driven approaches for patient stratification and treatment selection;
- AI algorithms for personalized drug discovery and development;
- Ethical considerations and challenges in implementing ML-based personalized medicine;
- Integration of ML tools with electronic health records and clinical workflows;
- Novel ML techniques for analyzing multi-omics data in personalized medicine.
By compiling these cutting-edge studies, we aim to provide a comprehensive overview of the current state and future directions of ML in personalized medicine, fostering interdisciplinary collaboration and advancing the field towards more precise and effective patient care.
Dr. Josip Vrdoljak
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
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 Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- LLMs in medicine
- machine learning
- deep learning
- drug discovery
- personalized medicine
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