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

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: 30 June 2025 | Viewed by 1643

Special Issue Editor

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

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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 (1 paper)

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Research

12 pages, 1410 KiB  
Article
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 1 | Viewed by 1102
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, [...] Read more.
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
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