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Diagnostics
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Artificial Intelligence in the Service of Medicine: From Pathological Diagnosis...
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Artificial Intelligence in the Service of Medicine: From Pathological Diagnosis to Forensic Pathology

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: 31 July 2026 | Viewed by 435

Special Issue Editor

Dr. Gelsomina Mansueto

E-Mail Website
Guest Editor
Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania “Luigi Vanvitelli”, Piazza L. Miraglia 2, 80138 Naples, Italy
Interests: cardiovascular pathology; neuropathology; forensic pathology; histopathology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is reshaping modern medical diagnostics, offering unprecedented opportunities for higher accuracy, faster workflows, and deeper insights across multiple branches of pathology. This Special Issue focuses on the transformative applications of AI in both clinical pathology and forensic pathology, highlighting how intelligent systems are enhancing diagnostic precision and supporting complex case evaluations.

In pathology, AI-driven algorithms are revolutionizing tissue examination, improving image interpretation, and reducing inter-observer variability. These technologies assist pathologists in detecting subtle morphological patterns, quantifying biomarkers, and streamlining diagnostic processes.

In forensic pathology, AI is emerging as a powerful tool for analysing postmortem findings, supporting the interpretation of injury patterns, estimating time-related biological changes, and improving the consistency and objectivity of forensic assessments. By augmenting expert judgment, AI contributes to more standardized and data-driven evaluations in medicolegal death investigations.

While AI offers transformative advantages, its integration also presents challenges related to data quality, transparency, validation, and clinical acceptance. This Special Issue brings together leading researchers to explore the latest innovations, practical applications, and key considerations for implementing AI across clinical and forensic pathology.

Our goal is to illuminate how artificial intelligence can effectively support pathologists in different fields of medicine such as cardiopathology, neuropathology, and forensic settings, advancing diagnostic science and contributing to improving our understanding of disease and death.

Dr. Gelsomina Mansueto
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

  • artificial intelligence in pathology
  • digital pathology
  • computational pathology
  • whole-slide imaging (WSI)
  • machine learning-based diagnostics
  • deep learning algorithms
  • pathology
  • cardiopathology
  • neuropathology
  • forensic pathology
  • postmortem imaging analysis
  • biomarker detection and quantification

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Published Papers (1 paper)

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Research

30 pages, 3891 KB  
Article
A Calibrated Deep Learning Framework Integrating Spatial Annotations and Clinical Metadata for Safe Three-Class Bone Lesion Classification on Radiographs
by Mert Ocak and Cumali Çatak
Diagnostics 2026, 16(12), 1811; https://doi.org/10.3390/diagnostics16121811 - 11 Jun 2026
Viewed by 104
Abstract
Background/Objectives: Accurate bone lesion classification on radiographs is critical for clinical decision-making and forensic identification. Existing deep learning approaches treat radiographs as whole images, neglecting available spatial annotations and clinical metadata. To develop an ROI-guided deep learning framework integrating clinical metadata for [...] Read more.
Background/Objectives: Accurate bone lesion classification on radiographs is critical for clinical decision-making and forensic identification. Existing deep learning approaches treat radiographs as whole images, neglecting available spatial annotations and clinical metadata. To develop an ROI-guided deep learning framework integrating clinical metadata for three-class (Normal, Benign, Malignant) bone lesion classification and to assess its clinical safety profile. Methods: Using the BTXRD (3746 radiographs: 1879 Normal, 1525 Benign, 342 Malignant), an EfficientNetV2-S backbone was combined with an 11-dimensional metadata MLP trained on ROI-cropped regions. Training employed Focal Loss with adaptive class weighting, Mixup/CutMix augmentations, Stochastic Weight Averaging, and Test-Time Augmentation. Five-fold stratified cross-validation with bootstrap confidence intervals (n = 2000) and probability calibration metrics were used. Results: The framework achieved 96.05% accuracy (95% CI: 95.41–96.66%), 93.94% balanced accuracy, 92.62% macro F1-score, and 99.21% macro-AUC (95% CI: 98.89–99.42%). Critically, near-zero Malignant-to-Normal misclassifications occurred (1/342, 0.29%; 95% Clopper–Pearson CI: 0.01–1.62%) across all 3746 predictions. The minority Malignant class attained F1 = 83.53% despite comprising only 9.1% of the dataset. Conclusions: ROI-guided deep learning with metadata fusion achieves state-of-the-art bone lesion classification with clinically safe error patterns and probability outputs whose calibration was explicitly quantified, supporting its potential as a decision support tool in diagnostic radiology and forensic anthropology, pending external validation on independent cohorts. Full article
(This article belongs to the Special Issue Artificial Intelligence in the Service of Medicine: From Pathological Diagnosis to Forensic Pathology)
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