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Artificial Intelligence for Personalized Medicine: Bridging Innovative Technologies and Patient-Centric Care

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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: 31 May 2026 | Viewed by 21478

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Special Issue Editor

Dr. Agostino Forestiero

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Guest Editor

Institute for High-Performance Computing and Networking, National Research Council of Italy, 87036 Cosenza, Italy
Interests: artificial intelligence; personalized medicine; patient-centric care; healthcare IoT; biomedical data integration; precision health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the transformative potential of artificial intelligence (AI) in personalized medicine, focusing on cutting-edge research, innovations and practical applications that enhance patient care through customization. It will cover a range of topics, from AI-driven diagnostics and treatment recommendations to data integration and ethical considerations in AI applications.

Objectives:

To showcase the latest advancements in AI technologies that contribute to personalized medical approaches;
To discuss the integration of AI with genomics, proteomics and other omics technologies for comprehensive patient profiling;
To present case studies and real-world applications demonstrating the impact of AI on patient outcomes.

The topics of interest are as follows (but not limited to): AI and machine/deep learning algorithms for predictive modeling in disease diagnosis and prognosis, eXplainable AI (XAI) for personalized medicine applications, integration of electronic health records (EHRs) and patient data for personalized treatment plans, AI in drug discovery and development for personalized therapy, AI to interpret complex genomic data and mutation patterns, deep/machine learning models that predict individual patient responses, and wearables and IoT devices in monitoring and managing health conditions.

Dr. Agostino Forestiero
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 250 words) can be sent to the Editorial Office for assessment.

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

artificial intelligence (AI)
personalized medicine
AI-driven diagnostics and treatment
personalized treatment
AI applications
genomics, proteomics and other omics technologies
machine/deep learning algorithms
drug discovery and development for personalized therapy
complex genomic data and mutation patterns
wearables and ioT devices

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Published Papers (8 papers)

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Research

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10 pages, 353 KB

Open AccessArticle

Clinical Application of Artificial Intelligence in Anesthesiology: A Multicenter Retrospective Comparison Between Human Anesthetic Decisions and Algorithmic Recommendations in Non-Cardiac Surgery

by Gilberto Duarte-Medrano, Natalia Nuño-Lámbarri, Octavio Gonzalez-Chon, Rebeca Garazi Elguezabal Rodelo, Carmelo Calvagna, Daniele Paternò, Luigi La Via and Massimiliano Sorbello

J. Pers. Med. 2026, 16(4), 222; https://doi.org/10.3390/jpm16040222 - 17 Apr 2026

Viewed by 522

Abstract

Background: Artificial intelligence (AI) is progressively entering perioperative medicine; however, its role in preoperative anesthetic decision-making remains insufficiently characterized. We evaluated the concordance between anesthesiologist-selected anesthetic techniques and algorithm-generated recommendations in a cohort of adult patients undergoing non-cardiac surgery. Methods: This retrospective observational study included adult patients (≥18 years) undergoing elective non-cardiac surgery between January 2024 and January 2025 at two international centers (Mexico and Italy). Clinical, demographic, and surgical variables were extracted from electronic medical records. For each case, a structured anonymized vignette was submitted to ChatGPT (version 5.0, medical configuration) to obtain an independent recommendation regarding anesthetic technique. Concordance between AI-generated and clinician-selected techniques was assessed using agreement analysis and stratified by country and surgical specialty. Results: A total of 1965 patients were analyzed. Overall concordance between ChatGPT recommendations and anesthesiologist-selected techniques was 84.6%. Agreement remained stable across centers (Mexico 84.3%; Italy 88.7%). Disagreement rates varied by surgical specialty, with the highest values observed in vascular and proctologic surgery (28.6%), followed by urology (21.1%) and thoracic surgery (18.8%). Orthopedic procedures—particularly shoulder arthroscopy—accounted for a relevant proportion of divergences, where AI frequently favored regional techniques over general anesthesia. No specialty demonstrated discordance exceeding 30%. Conclusions: AI-generated anesthetic recommendations demonstrated substantial concordance with expert clinical decision-making across heterogeneous surgical settings. These findings support the potential integration of AI within a hybrid decision-making framework, complementing—rather than replacing—anesthesiologist expertise in contemporary perioperative care. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalized Medicine: Bridging Innovative Technologies and Patient-Centric Care)

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12 pages, 1410 KB

Open AccessArticle

Comparative Analysis of Large Language Models in Emergency Plastic Surgery Decision-Making: The Role of Physical Exam Data

by Sahar Borna, Cesar A. Gomez-Cabello, Sophia M. Pressman, Syed Ali Haider and Antonio Jorge Forte

J. Pers. Med. 2024, 14(6), 612; https://doi.org/10.3390/jpm14060612 - 8 Jun 2024

Cited by 11 | Viewed by 3439

Abstract

In the U.S., diagnostic errors are common across various healthcare settings due to factors like complex procedures and multiple healthcare providers, often exacerbated by inadequate initial evaluations. This study explores the role of Large Language Models (LLMs), specifically OpenAI’s ChatGPT-4 and Google Gemini, in improving emergency decision-making in plastic and reconstructive surgery by evaluating their effectiveness both with and without physical examination data. Thirty medical vignettes covering emergency conditions such as fractures and nerve injuries were used to assess the diagnostic and management responses of the models. These responses were evaluated by medical professionals against established clinical guidelines, using statistical analyses including the Wilcoxon rank-sum test. Results showed that ChatGPT-4 consistently outperformed Gemini in both diagnosis and management, irrespective of the presence of physical examination data, though no significant differences were noted within each model’s performance across different data scenarios. Conclusively, while ChatGPT-4 demonstrates superior accuracy and management capabilities, the addition of physical examination data, though enhancing response detail, did not significantly surpass traditional medical resources. This underscores the utility of AI in supporting clinical decision-making, particularly in scenarios with limited data, suggesting its role as a complement to, rather than a replacement for, comprehensive clinical evaluation and expertise. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalized Medicine: Bridging Innovative Technologies and Patient-Centric Care)

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Review

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17 pages, 1826 KB

Open AccessReview

Integrating AI Segmentation, Simulated Digital Twins, and Extended Reality into Medical Education: A Narrative Technical Review and Proof-of-Concept Case Study

by Parhesh Kumar, Ingharan Siddarthan, Catharine Kelsh Keim, Daniel K. Cho, John E. Rubin, Robert S. White and Rohan Jotwani

J. Pers. Med. 2026, 16(4), 202; https://doi.org/10.3390/jpm16040202 - 3 Apr 2026

Viewed by 816

Abstract

Background/Objectives: Simulation digital twins (DT) models that integrate patient-specific imaging with artificial intelligence (AI)-based segmentation and extended reality (XR) technologies are rapidly increasing in relevance in personalized medicine. While their clinical applications are expanding, their role as reusable educational tools and the technical pipeline utilized for their development remain incompletely characterized. This narrative review examines current approaches to digital twin creation and XR integration, illustrated by a scoliosis-specific proof-of-concept educational case study. Methods: A narrative technical review was conducted by identifying relevant search keywords within the fields of AI-based image segmentation, extended reality in medicine, and medical education based on the authors’ expertise and familiarity with the subject. PubMed, Google Scholar, and Scopus were searched for English-language studies published primarily between 2015 and 2025 addressing patient-specific three-dimensional modeling, AI-driven segmentation, and XR applications in spine, orthopedic, anesthesiology, and interventional care. A de-identified case of scoliosis is used to present a proof-of-concept example of this process of creating a simulated digital twin for the purpose of medical education in a recorded XR format. Results: Prior studies demonstrated benefits of patient-specific 3D models for anatomical understanding and procedural planning, while highlighting limitations in segmentation accuracy and workflow integration. Nevertheless, while DTs have traditionally served clinical roles in surgical planning or pre-procedural rehearsal, their pedagogical potential remains under-explored. In the proof-of-concept case study, AI-assisted segmentation enabled rapid creation of an anatomically detailed scoliosis digital twin that was incorporated into XR and used to produce a reusable, spatially anchored instructional experience focused on neuraxial access. Conclusions: AI-enabled digital twin models integrated with XR represent a promising approach for personalized, anatomy-driven medical education. Further evaluation is needed to assess educational outcomes, scalability, and integration into clinical training workflows. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalized Medicine: Bridging Innovative Technologies and Patient-Centric Care)

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18 pages, 2168 KB

Open AccessReview

Artificial Intelligence in Transcriptomics: From Human-in-the-Loop to Agentic AI

by Giulia Gentile, Giovanna Morello, Valentina La Cognata, Maria Guarnaccia and Sebastiano Cavallaro

J. Pers. Med. 2026, 16(4), 181; https://doi.org/10.3390/jpm16040181 - 27 Mar 2026

Viewed by 1422

Abstract

To better understand the complexity of biological systems, research has shifted from a reductionist to a holistic approach, expanding the focus from single genes to a genome-scale view of gene activity and regulation. This is known as transcriptomics, a continuously growing field generating gene expression signatures from different technologies. A comparable paradigm shift has occurred in computational systems biology with the implementation of Artificial Intelligence (AI) learning models for gene expression analysis and integration. These models enable transcriptome-based profiling to address challenges of data heterogeneity, integration, and updating, assisting human intelligence and enhancing their ability to retrieve, analyze, integrate, and generate data recursively, thanks to their intrinsic predictive, inferential, reinforcement, and generative capabilities. Additionally, while scientists worldwide are still learning how to leverage AI methods that can maintain the human-in-the-loop, a new fundamental change is emerging: agentic AI, which can autonomously act and employ other AI methods to pursue its objectives. As a futuristic perspective, the proposed data analysis pipeline imagines agentic AI systems allowing the automated retrieval and pre-processing of heterogeneous transcriptomics data, analysis and integration with other omics datasets, performed with an incremental updating and recurrent analysis (IURA) model that could allow the detection of guideline updates (e.g., disease reclassification) and the generation of new hypotheses, such as candidate biomarkers or transcriptome–phenotype correlations. Since personalized medicine could derive profound benefits from its use, this scenario also raises important considerations regarding the advantages and concerns associated with the use of scientific AI agents in research and clinical practice. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalized Medicine: Bridging Innovative Technologies and Patient-Centric Care)

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13 pages, 741 KB

Open AccessReview

Model-Informed Precision Dosing: Conceptual Framework for Therapeutic Drug Monitoring Integrating Machine Learning and Artificial Intelligence Within Population Health Informatics

by Jennifer Le, Hien N. Le, Giang Nguyen, Rebecca Kim, Sean N. Avedissian, Connie Vo, Ba Hai Le, Thanh Hai Nguyen, Dua Thi Nguyen, Dylan Huy Do, Brian Le, Austin-Phong Nguyen, Tu Tran, Chi Kien Phung, Duong Anh Minh Vu, Karandeep Singh and Amy M. Sitapati

J. Pers. Med. 2026, 16(2), 76; https://doi.org/10.3390/jpm16020076 - 31 Jan 2026

Cited by 1 | Viewed by 1245

Abstract

Background/Objective: Traditional therapeutic drug monitoring is limited by manual interpretation and specific constraints like sampling at steady-state and requiring a minimum of two drug concentrations. The integration of model-informed precision dosing (MIPD) into population health informatics represents a promising approach to address drug safety and efficacy. This article explored the integration of MIPD within population health informatics and evaluated its potential to enhance precision dosing using artificial intelligence (AI), machine learning (ML), and electronic health records (EHRs). Methods: PubMed and Embase searches were conducted, and all relevant peer-reviewed studies in English published between 1958 and December 2024 were included if they pertained to MIPD and population-level health, with the use of AI/ML algorithms to predict individualized drug dosing requirements. Emphasis was placed on vulnerable populations such as critically-ill, geriatric, and pediatric groups. Results: MIPD with the Bayesian method represents a scalable innovation in precision medicine, with significant implications for population health informatics. By combining AI/ML with comprehensive electronic health records (EHRs), MIPD can offer real-time, precise dosing adjustments. This integration has the potential to improve patient safety, optimize therapeutic outcomes, and reduce healthcare costs, especially for vulnerable populations where evidence is limited. Successful implementation requires collaboration among clinicians, pharmacists, and health informatics professionals, alongside secure data management and interoperability solutions. Conclusions: Further research is needed to define, implement, and evaluate practical applications of AI/ML. This insight may help develop standards and identify drugs for MIPD to advance personalized medicine within population health informatics. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalized Medicine: Bridging Innovative Technologies and Patient-Centric Care)

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Graphical abstract

11 pages, 3093 KB

Open AccessReview

Artificial Intelligence and 3D Reconstruction in Complex Hepato-Pancreato-Biliary (HPB) Surgery: A Comprehensive Review of the Literature

by Andreas Panagakis, Ioannis Katsaros, Maria Sotiropoulou, Adam Mylonakis, Markos Despotidis, Aristeidis Sourgiadakis, Panagiotis Sakarellos, Stylianos Kapiris, Chrysovalantis Vergadis, Dimitrios Schizas, Evangelos Felekouras and Michail Vailas

J. Pers. Med. 2025, 15(12), 610; https://doi.org/10.3390/jpm15120610 - 8 Dec 2025

Cited by 2 | Viewed by 1065

Abstract

Background: The management of complex hepato-pancreato-biliary (HPB) pathologies demands exceptional surgical precision. Traditional two-dimensional imaging has limitations in depicting intricate anatomical relationships, potentially complicating preoperative planning. This review explores the synergistic application of three-dimensional (3D) reconstruction and artificial intelligence (AI) to support surgical decision-making in complex HPB cases. Methods: This narrative review synthesized the existing literature on the applications, benefits, limitations, and implementation challenges of 3D reconstruction and AI technologies in HPB surgery. Results: The literature suggests that 3D reconstruction provides patient-specific, interactive models that significantly improve surgeons’ understanding of tumor resectability and vascular anatomy, contributing to reduced operative time and blood loss. Building upon this, AI algorithms can automate image segmentation for 3D modeling, enhance diagnostic accuracy, and offer predictive analytics for postoperative complications, such as liver failure. By analyzing large datasets, AI can identify subtle risk factors to guide clinical decision-making. Conclusions: The convergence of 3D visualization and AI-driven analytics is contributing to an emerging paradigm shift in HPB surgery. This combination may foster a more personalized, precise, and data-informed surgical approach, particularly in anatomically complex or high-risk cases. However, current evidence is heterogeneous and largely observational, underscoring the need for prospective multicenter validation before routine implementation. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalized Medicine: Bridging Innovative Technologies and Patient-Centric Care)

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19 pages, 272 KB

Open AccessReview

Artificial Intelligence in the Diagnosis of Pediatric Rare Diseases: From Real-World Data Toward a Personalized Medicine Approach

by Nikola Ilić and Adrijan Sarajlija

J. Pers. Med. 2025, 15(9), 407; https://doi.org/10.3390/jpm15090407 - 1 Sep 2025

Cited by 4 | Viewed by 3940

Abstract

Background: Artificial intelligence (AI) is increasingly applied in the diagnosis of pediatric rare diseases, enhancing the speed, accuracy, and accessibility of genetic interpretation. These advances support the ongoing shift toward personalized medicine in clinical genetics. Objective: This review examines current applications of AI in pediatric rare disease diagnostics, with a particular focus on real-world data integration and implications for individualized care. Methods: A narrative review was conducted covering AI tools for variant prioritization, phenotype–genotype correlations, large language models (LLMs), and ethical considerations. The literature was identified through PubMed, Scopus, and Web of Science up to July 2025, with priority given to studies published in the last seven years. Results: AI platforms provide support for genomic interpretation, particularly within structured diagnostic workflows. Tools integrating Human Phenotype Ontology (HPO)-based inputs and LLMs facilitate phenotype matching and enable reverse phenotyping. The use of real-world data enhances the applicability of AI in complex and heterogeneous clinical scenarios. However, major challenges persist, including data standardization, model interpretability, workflow integration, and algorithmic bias. Conclusions: AI has the potential to advance earlier and more personalized diagnostics for children with rare diseases. Achieving this requires multidisciplinary collaboration and careful attention to clinical, technical, and ethical considerations. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalized Medicine: Bridging Innovative Technologies and Patient-Centric Care)

21 pages, 702 KB

Open AccessEditor’s ChoiceReview

The Role of Artificial Intelligence and Emerging Technologies in Advancing Total Hip Arthroplasty

by Luca Andriollo, Aurelio Picchi, Giulio Iademarco, Andrea Fidanza, Loris Perticarini, Stefano Marco Paolo Rossi, Giandomenico Logroscino and Francesco Benazzo

J. Pers. Med. 2025, 15(1), 21; https://doi.org/10.3390/jpm15010021 - 9 Jan 2025

Cited by 28 | Viewed by 7220

Abstract

Total hip arthroplasty (THA) is a widely performed surgical procedure that has evolved significantly due to advancements in artificial intelligence (AI) and robotics. As demand for THA grows, reliable tools are essential to enhance diagnosis, preoperative planning, surgical precision, and postoperative rehabilitation. AI applications in orthopedic surgery offer innovative solutions, including automated hip osteoarthritis (OA) diagnosis, precise implant positioning, and personalized risk stratification, thereby improving patient outcomes. Deep learning models have transformed OA severity grading and implant identification by automating traditionally manual processes with high accuracy. Additionally, AI-powered systems optimize preoperative planning by predicting the hip joint center and identifying complications using multimodal data. Robotic-assisted THA enhances surgical precision with real-time feedback, reducing complications such as dislocations and leg length discrepancies while accelerating recovery. Despite these advancements, barriers such as cost, accessibility, and the steep learning curve for surgeons hinder widespread adoption. Postoperative rehabilitation benefits from technologies like virtual and augmented reality and telemedicine, which enhance patient engagement and adherence. However, limitations, particularly among elderly populations with lower adaptability to technology, underscore the need for user-friendly platforms. To ensure comprehensiveness, a structured literature search was conducted using PubMed, Scopus, and Web of Science. Keywords included “artificial intelligence”, “machine learning”, “robotics”, and “total hip arthroplasty”. Inclusion criteria emphasized peer-reviewed studies published in English within the last decade focusing on technological advancements and clinical outcomes. This review evaluates AI and robotics’ role in THA, highlighting opportunities and challenges and emphasizing further research and real-world validation to integrate these technologies into clinical practice effectively. Full article

(This article belongs to the Special Issue Artificial Intelligence for Personalized Medicine: Bridging Innovative Technologies and Patient-Centric Care)

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