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Diagnostics
Special Issues
AI-Driven Precision Medicine: Innovations in Diagnosis, Prognosis, and Management Response
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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 948

Special Issue Editor

Prof. Dr. Feng Gao

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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 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. 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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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

20 pages, 1535 KB  
Article
ConvNeXt-Driven Detection of Alzheimer’s Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes
by Mahdiyeh Basereh, Matthew Alexander Abikenari, Sina Sadeghzadeh, Trae Dunn, René Freichel, Prabha Siddarth, Dara Ghahremani, Helen Lavretsky and Vivek P. Buch
Diagnostics 2025, 15(23), 2997; https://doi.org/10.3390/diagnostics15232997 - 25 Nov 2025
Abstract
Background: Alzheimer’s disease (AD) is a leading worldwide cause of cognitive impairment, necessitating accurate, inexpensive diagnostic tools to enable early recognition. Methods: In this study, we present a robust deep learning approach for AD classification based on structural MRI scans, ConvNeXt, an emergent [...] Read more.
Background: Alzheimer’s disease (AD) is a leading worldwide cause of cognitive impairment, necessitating accurate, inexpensive diagnostic tools to enable early recognition. Methods: In this study, we present a robust deep learning approach for AD classification based on structural MRI scans, ConvNeXt, an emergent convolutional architecture inspired by vision transformers. We introduce AlzaSet, a clinically curated T1-weighted MRI dataset of 79 subjects (63 with Alzheimer’s disease [AD], 16 cognitively normal controls [NC]) acquired on a 1.5 T Siemens Aera in axial, coronal, and sagittal planes, respectively (12,947 slices in total). Images are neuroradiologist-labeled. Results are reported per plane, with awareness of the class imbalance at the subject level. We further present AlzaSet, a novel, expertly labeled clinical dataset with axial, coronal, and sagittal perspectives from AD and cognitively normal control subjects. Three ConvNeXt sizes (Tiny, Small, Base) were compared and benchmarked against existing state-of-the-art CNN models (VGG16, VGG19, InceptionV3, DenseNet121). Results: ConvNeXt-Base consistently outperformed the other models on coronal slices with an accuracy of 98.37% and an AUC of 0.992. Coronal views were determined to be most diagnostically informative, with emphasis on visualization of the medial temporal lobe. Moreover, comparison with recent ensemble-based techniques showed superior performance with comparable computational efficiency. Conclusions: These results indicate that ConvNeXt-capable models applied to clinically curated datasets have strong potential to provide scalable, real-time AD screening in diverse settings, including both high-resource and resource-constrained settings. Full article
(This article belongs to the Special Issue AI-Driven Precision Medicine: Innovations in Diagnosis, Prognosis, and Management Response)
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16 pages, 3440 KB  
Article
Multimodal-Imaging-Based Interpretable Deep Learning Framework for Distinguishing Brucella from Tuberculosis Spondylitis: A Dual-Center Study
by Mayidili Nijiati, Mei Zhang, Chencui Huang, Xinyue Chou, Lingyan Shen, Haiting Ma, Zhenwei Ren, Maimaitishawutiaji Maimaiti, Yi You, Xiaoguang Zou and Yunling Wang
Diagnostics 2025, 15(23), 2963; https://doi.org/10.3390/diagnostics15232963 - 22 Nov 2025
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Abstract
Objectives: Brucella spondylitis (BS) and tuberculosis spondylitis (TS) are two causes of infection that share overlapping clinical and imaging features, complicating diagnoses. Early differentiation is critical, as treatment regimens differ significantly. This study aims to develop a deep learning framework using multimodal computed [...] Read more.
Objectives: Brucella spondylitis (BS) and tuberculosis spondylitis (TS) are two causes of infection that share overlapping clinical and imaging features, complicating diagnoses. Early differentiation is critical, as treatment regimens differ significantly. This study aims to develop a deep learning framework using multimodal computed tomography (CT) and magnetic resonance imaging (MRI) data to accurately distinguish between these two conditions, improving diagnostic accuracy and patient outcomes. Methods: In this study, imaging data were acquired from two centers using different MRI and CT protocols. Sagittal T1-weighted (T1WI) and T2-weighted imaging (T2WI), fat-suppression sequences (T2WI FSE), and sagittal CT data were collected. Image preprocessing included region of interest (ROI) segmentation, and normalization and augmentation techniques were used. A deep learning model, based on pre-trained GoogleNet architectures, was trained and evaluated against human radiologists using metrics including accuracy, sensitivity, and AUC to assess diagnostic performance. Results: In this study, the GoogleNet deep learning model outperformed other architectures in classifying TS and BS, achieving AUCs of 95.97%, 91.24%, and 81.25% across training, test, and external validation datasets, respectively. In contrast, ResNet, DenseNet, and EfficientNet models showed lower AUC values. GoogleNet also demonstrated high accuracy (90.77% training, 83.04% test) and 90.91% sensitivity and 61.11% specificity in external validation. When compared to three radiologists, GoogleNet outperformed in diagnostic accuracy and speed, achieving an AUC of 88.01% and processing cases in 0.001 min. These findings highlight the potential of AI to enhance diagnostic performance and efficiency. Lastly, the explanation provided by the Grad-Cam model precisely localized major lesions. Conclusions: This multimodal-imaging-based deep learning model could well differentiate TS and BS. Deep learning does not need manual feature extraction, selection, or model development, and has great potential in daily clinical practice. Full article
(This article belongs to the Special Issue AI-Driven Precision Medicine: Innovations in Diagnosis, Prognosis, and Management Response)
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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
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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
(This article belongs to the Special Issue AI-Driven Precision Medicine: Innovations in Diagnosis, Prognosis, and Management Response)
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