AI-Driven Precision Medicine: Innovations in Diagnosis, Prognosis, and Management Response

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 185

Special Issue Editor


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Guest Editor
1. Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
2. Guangdong Institute of Gastroenterology, Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangzhou, China
Interests: artificial intelligence; colorectal cancer; genomics; translational medicine
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Special Issue Information

Dear Colleagues,

The management of complex diseases remains a significant global health challenge, with their inherent heterogeneity demanding more effective and personalized clinical strategies. The integration of artificial intelligence (AI) is rapidly transforming the landscape of healthcare, offering unprecedented opportunities for innovation in diagnostics, prognostics, and therapeutic guidance. This Special Issue aims to bring together cutting-edge research on AI-powered solutions that advance the paradigm of precision medicine. We welcome original research articles and comprehensive reviews that explore the development, validation, and clinical application of AI algorithms.

Topics of interest include, but are not limited to, the following:

  • AI in medical image analysis (e.g., radiology, pathology) for lesion detection, segmentation, and classification.
  • The application of large language models (LLMs) for structuring electronic health records and generating clinical decision support insights.
  • The use of machine learning for predicting treatment response and identifying resistance mechanisms from multi-omics data.
  • The development of novel prognostic and predictive biomarkers using deep learning.
  • AI-powered tools for personalized risk stratification and disease progression modeling.

This collection seeks to showcase how AI is paving the way for more precise, personalized, and timely management across a wide spectrum of diseases.

Prof. Dr. Feng Gao
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. Diagnostics is an international peer-reviewed open access semimonthly 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

  • precision medicine
  • artificial intelligence
  • clinical decision support
  • deep learning
  • large language models (LLMs)
  • prognosis prediction
  • medical imaging

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Published Papers (1 paper)

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Research

12 pages, 1954 KB  
Article
Two-Stage Hierarchical Pruning (THP-CNN) of Convolutional Neural Networks for Rapid Pathogenic Bacterial Detection Using High-Resolution Colony Images in Intensive Care Units
by Can Xie and Kefeng Li
Diagnostics 2025, 15(18), 2349; https://doi.org/10.3390/diagnostics15182349 - 16 Sep 2025
Viewed by 98
Abstract
Background/Objectives: Patients in Intensive Care Units (ICUs) have an elevated risk of infection. Accurate identification of pathogenic bacteria is critical for targeted interventions; however, convolutional neural networks (CNNs) face challenges of high computational demands and parameter redundancy. Methods: We developed a [...] Read more.
Background/Objectives: Patients in Intensive Care Units (ICUs) have an elevated risk of infection. Accurate identification of pathogenic bacteria is critical for targeted interventions; however, convolutional neural networks (CNNs) face challenges of high computational demands and parameter redundancy. Methods: We developed a two-stage hierarchical pruning framework for CNN compression (THP-CNN), combining channel importance estimation with receptive field equivalence transformation for a 24-class pathogenic bacteria classification task. Results: THP-CNN (70% pruned) achieves an accuracy of 0.86 with 0.62 M parameters, outperforming ResNet-50 (0.72), MobileNet V2 (0.81), Inception (0.74), and AlexNet (0.62), with the 50% and 60% pruned variants in cross-validation stably maintaining a mean accuracy of 0.79. Conclusions: THP-CNN demonstrates potential for lightweight, real-time bacterial classification, offering a computationally efficient solution for automated pathogen detection. Full article
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