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Enhancing Genetic Prediction in Precision Medicine with Artificial Intelligence
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Enhancing Genetic Prediction in Precision Medicine with Artificial Intelligence

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Technologies and Resources for Genetics".

Deadline for manuscript submissions: closed (20 October 2025) | Viewed by 1777

Special Issue Editor

Prof. Dr. Isabel De la Torre Diez

E-Mail Website
Guest Editor
Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, Paseo de Belen, 15, 47011 Valladolid, Spain
Interests: mHealth; eHealth; telemedicine; AI (artificial intelligence) in medicine

Special Issue Information

Dear Colleagues,

Genes is pleased to announce a Special Issue entitled “Enhancing Genetic Prediction in Precision Medicine with Artificial Intelligence”, for which we are looking for original research papers. Precision medicine represents an emerging approach to clinical research and patient care, focusing on understanding and treating diseases. It integrates multimodal or multi-omics data to make tailored decisions for each patient. Within artificial intelligence (AI), machine learning is a computing methodology that aims to identify complex patterns in data. Machine learning analysis in precision medicine enables the comprehensive analysis of large datasets and a deeper understanding of human health and disease. This Special Issue will include works exploring applications of AI in precision medicine to improve patient care. Research areas may include, but are not limited to, the analysis of gene expression patterns, phenotype prediction, prevention, diagnosis, risk score assessment, patients risk categorization, and treatment for each individual based on individual genetics and/or epigenetics, among other relevant topics.

Prof. Dr. Isabel De la Torre Diez
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. Genes 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

  • AI/ML-based precision medicine models and applications
  • integration of nanomaterials into AI-based precision medicine
  • translating big data into healthcare: opportunities, challenges, and legal aspects
  • synergy between AI and precision medicine
  • multi-omics profiling
  • phenotype prediction
  • risk analysis in relation to genomics in personalized medicine
  • ML and genomics

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Published Papers (1 paper)

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Research

17 pages, 276 KB  
Article
The Artificial Intelligence-Assisted Diagnosis of Skeletal Dysplasias in Pediatric Patients: A Comparative Benchmark Study of Large Language Models and a Clinical Expert Group
by Nikola Ilić, Nina Marić, Dimitrije Cvetković, Marko Bogosavljević, Gordana Bukara-Radujković, Jovana Krstić, Zoran Paunović, Ninoslav Begović, Sanja Panić Zarić, Slađana Todorović, Katarina Mitrović, Aleksandar Vlahović and Adrijan Sarajlija
Genes 2025, 16(7), 762; https://doi.org/10.3390/genes16070762 - 28 Jun 2025
Cited by 2 | Viewed by 1252
Abstract
Background/Objectives: Skeletal dysplasias are a heterogeneous group of rare genetic disorders with diverse and overlapping clinical presentations, posing diagnostic challenges even for experienced clinicians. With the increasing availability of artificial intelligence (AI) in healthcare, large language models (LLMs) offer a novel opportunity to [...] Read more.
Background/Objectives: Skeletal dysplasias are a heterogeneous group of rare genetic disorders with diverse and overlapping clinical presentations, posing diagnostic challenges even for experienced clinicians. With the increasing availability of artificial intelligence (AI) in healthcare, large language models (LLMs) offer a novel opportunity to assist in rare disease diagnostics. This study aimed to compare the diagnostic accuracy of two advanced LLMs, ChatGPT (version GPT-4) and DeepSeek, with that of a clinical expert panel in a cohort of pediatric patients with genetically confirmed skeletal dysplasias. Methods: We designed a prospective vignette-based diagnostic benchmarking study including 45 children with confirmed skeletal dysplasias from two tertiary centers. Both LLMs were prompted to provide primary and differential diagnoses based on standardized clinical case vignettes. Their outputs were compared with those of two human experts (a pediatric endocrinologist and a pediatric orthopedic surgeon), using molecular diagnosis as the gold standard. Results: ChatGPT and DeepSeek achieved a comparable diagnostic top-3 accuracy (62.2% and 64.4%, respectively), with a high intermodel agreement (Cohen’s κ = 0.95). The expert panel outperformed both models (82.2%). While LLMs performed well on more common disorders, they struggled with ultra-rare and multisystemic conditions. In one complex case missed by experts, the DeepSeek model successfully proposed the correct diagnosis. Conclusions: LLMs offer a complementary diagnostic value in skeletal dysplasias, especially in under-resourced medical settings. Their integration as a supportive tool in multidisciplinary diagnostic workflows may enhance early recognition and reduce diagnostic delays in rare disease care. Full article
(This article belongs to the Special Issue Enhancing Genetic Prediction in Precision Medicine with Artificial Intelligence)
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