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Life
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Artificial Intelligence and Machine Learning for Biomedical Diagnostics and Prognostics
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Artificial Intelligence and Machine Learning for Biomedical Diagnostics and Prognostics

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 1846

Special Issue Editors

Dr. César Byron Guevara Maldonado

E-Mail Website
Guest Editor
Quantitative Methods Department, CUNEF Universidad, 28040 Madrid, Spain
Interests: artificial intelligence; data mining; evolutionary algorithms; computer vision; reinforcement
Dr. Victoria López

E-Mail Website
Guest Editor
Quantitative Methods Department, CUNEF Universidad, 28040 Madrid, Spain
Interests: computational mathematics; computational intelligence; artificial intelligence; algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Diego Fernando Riofrío Luzcando

E-Mail Website
Guest Editor
Quantitative Methods Department, CUNEF Universidad, 28040 Madrid, Spain
Interests: computer science; HCI; virtual reality; e-learning; inteligent tutoring systems

Special Issue Information

Dear Colleagues,

This Special Issue invites original research and reviews on the design, development, and validation of Artificial Intelligence (AI) and Machine Learning (ML) methods applied to detection, diagnosis, risk stratification, and prediction of clinical outcomes. We welcome a broad range of study designs, including individually randomized, cluster‐randomized, and stepped-wedge trials; pragmatic and adaptive trials; cohort, case–control, cross-sectional, and longitudinal studies; N-of-1 trials and interrupted time-series analyses; diagnostic and prognostic accuracy studies; clinical impact and decision-analytic evaluations; as well as systematic reviews and meta-analyses.

Submissions are encouraged across psychology and psychiatry (e.g., ecological momentary assessment, relapse prediction, suicide risk modeling, treatment response to psychotherapies and pharmacotherapies, neuroimaging, and speech/language analytics); physiotherapy and rehabilitation (e.g., telerehabilitation trials, gait analysis using inertial sensors, and exercise protocols); and nutrition (e.g., controlled dietary interventions, crossover feeding studies, nutrigenomics, and metabolomics for personalized nutrition). Additional medical domains of interest include cardiology (ECG and wearable sensors), oncology (prognosis and treatment response), sleep medicine, critical care, pediatrics, and geriatrics.

We particularly value multimodal data integration (medical imaging, omics, digital pathology, wearable sensors, and electronic health records), supervised and unsupervised learning, transfer learning, generative models, and explainable/interpretable approaches. Submissions should demonstrate clinical utility, robust calibration, multicenter generalizability, fairness, privacy and safety safeguards, and—where possible—open datasets, reproducible code, external validation, comparison to clinical standards, and prospective evaluation to accelerate the translation of AI into measurable health impact.

Dr. César Byron Guevara Maldonado
Dr. Victoria López
Dr. Diego Fernando Riofrío Luzcando
Guest Editors

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. Life 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

  • artificial intelligence
  • machine learning
  • diagnostic and prognostic modeling
  • multimodal data integration
  • explainable AI

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

Published Papers (2 papers)

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Research

17 pages, 892 KB  
Article
Artificial Intelligence for Biomedical Diagnostics: Diagnostic Accuracy and Reliability of Multimodal Large Language Models in Electrocardiogram Interpretation
by Henrik Stelling, Armin Kraus, Gerrit Grieb, David Breidung and Ibrahim Güler
Life 2026, 16(4), 681; https://doi.org/10.3390/life16040681 - 16 Apr 2026
Viewed by 642
Abstract
The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study [...] Read more.
The electrocardiogram (ECG) is a central tool in cardiovascular diagnostics, yet interpretation requires expertise and remains subject to variability. Multimodal large language models (MLLMs) have shown emerging capabilities in medical image analysis, but their performance in ECG interpretation remains insufficiently characterized. This study evaluated the diagnostic accuracy and inter-run reliability of five MLLMs across ECG interpretation tasks. Thirteen standard 12-lead ECGs were presented to five models (ChatGPT-5.3, Gemini 3.1 Pro, Claude Opus 4.6, Grok 4.1, and ERNIE 5.0) across five independent runs per case, yielding 2275 task-level assessments. Six categorical interpretation tasks (rhythm, electrical axis, PR/P-wave morphology, QRS duration, ST/T-wave morphology, and QTc interval) were compared with expert-consensus ground truth, while heart rate estimation was evaluated using mean absolute error (MAE). Overall categorical accuracy ranged from 52.3% to 64.9%. QRS duration classification achieved the highest accuracy (66.2–90.8%), whereas ST/T-wave assessment showed the lowest performance (20.0–41.5%). Heart rate MAE ranged from 14.8 to 46.7 bpm. A dissociation between diagnostic accuracy and inter-run reliability was observed across models. These findings indicate that current MLLMs do not achieve clinically reliable ECG interpretation performance and highlight the importance of assessing diagnostic accuracy and inter-run reliability when evaluating artificial intelligence systems in biomedical diagnostics. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Biomedical Diagnostics and Prognostics)
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25 pages, 16496 KB  
Article
MassSeg-Framework: A Breast Mass Detection and Segmentation Framework Based on Deep Learning and an Active Contour Model
by Camila Zambrano, Noel Pérez-Pérez, Miguel Coimbra, Maria Baldeon-Calisto, Ricardo Flores-Moyano, José Ramón Mora, Oscar Camacho and Diego Benítez
Life 2026, 16(4), 653; https://doi.org/10.3390/life16040653 - 12 Apr 2026
Viewed by 643
Abstract
This work introduces the MassSeg-Framework, a fully automatic two-stage pipeline for breast mass analysis in mammography that integrates YOLOv11-based detection with Chan–Vese ACM refinement to achieve accurate mass localization and segmentation with a lightweight computational footprint. The framework was trained and evaluated [...] Read more.
This work introduces the MassSeg-Framework, a fully automatic two-stage pipeline for breast mass analysis in mammography that integrates YOLOv11-based detection with Chan–Vese ACM refinement to achieve accurate mass localization and segmentation with a lightweight computational footprint. The framework was trained and evaluated on two publicly available datasets using consistent experimental protocols. In the detection stage, YOLOv11-nano was the most effective architecture, with a confidence threshold of 0.4, achieving statistically significant mAP50 values of 0.862 and 0.709 on the dINbreast and dCBIS datasets, respectively. These results confirm that a moderate threshold preserves clinically relevant true-positive candidates, which is particularly important for screening-oriented settings where missed lesions are costly. In the segmentation stage, the proposed framework achieved mean DICE scores of 0.721 and 0.700 on the test sets of the same datasets, demonstrating consistent overlap with expert annotations. Compared with state-of-the-art approaches that commonly assume lesion-centered ROIs or rely on heavier backbones, the proposed pipeline addresses a more realistic scenario by performing automatic detection followed by segmentation while maintaining substantially lower computational requirements. This balance between performance and efficiency makes the MassSeg-Framework a promising tool for scalable mammography analysis, particularly in resource-constrained environments or high-throughput screening workflows that require rapid processing. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Biomedical Diagnostics and Prognostics)
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