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Machine Learning in Biomedical Sciences
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Machine Learning in Biomedical Sciences

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 3997

Special Issue Editors

Dr. Matteo Bodini

E-Mail Website
Guest Editor
Dipartimento di Economia, Management e Metodi Quantitativi, Università degli Studi di Milano, 20122 Milan, Italy
Interests: machine learning; deep learning; artificial intelligence; explainable AI; pattern recognition; signal processing
Dr. Giovanni Cugliari

E-Mail Website
Guest Editor
Department of Medical Sciences, Genomic Variation in Human Population and Complex Diseases Unit, University of Turin, 10126 Turin, Italy
Interests: cancer; genomics; artificial intelligence; machine learning; deep learning; advanced statistics
Special Issues, Collections and Topics in MDPI journals
Dr. Andrea Loddo

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
Interests: computer vision; image processing; machine learning; deep learning; artificial intelligence; medical image analysis; biomedical image analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning (ML) has emerged as a transformative tool in the biomedical sciences, offering unprecedented opportunities for analyzing complex biological data and enhancing patient care. The integration of ML with biomedical research has led to significant advancements in diagnostics, personalized medicine, drug discovery, and understanding of complex diseases. As we navigate through vast datasets, ranging from genomic sequences to clinical records, machine learning provides powerful techniques to uncover patterns, make predictions, and inform decision making.

The convergence of biomedical data with advanced ML techniques such as deep learning, natural language processing, and computer vision is revolutionizing how we approach medical research and healthcare delivery. This integration allows for more accurate diagnostic tools, predictive models for disease progression, and the development of innovative therapeutic strategies. Furthermore, ML algorithms are becoming essential in handling the ever-growing data from medical imaging, electronic health records, and high-throughput omic technologies.

This Special Issue on "Machine Learning in Biomedical Sciences" aims to collect the latest research and developments in this rapidly evolving field. We invite contributions that explore the application of machine learning methods to various aspects of biomedical sciences, including diagnostic systems, predictive analytics, personalized treatment plans, and biomedical data analysis. This Special Issue seeks to provide a platform for researchers and practitioners to discuss the challenges, innovations, and future directions of ML in the biomedical domain.

Recommended topics include the following:

  • Machine learning in medical imaging and diagnostics;
  • Predictive models for disease progression and patient outcomes;
  • Deep learning applications in genomics and proteomics;
  • Natural language processing (NLP) in clinical text analysis;
  • Integration of ML with electronic health records (EHRs) for personalized medicine;
  • Drug discovery and development using machine learning;
  • ML methods for analyzing high-throughput biological data;
  • Reinforcement learning in treatment planning and decision support;
  • Explainable AI in healthcare and biomedical research;
  • Ethical considerations and biases in ML applications in biomedicine;
  • Case studies on successful ML implementations in healthcare systems;
  • Real-time data analysis and decision making in medical settings;
  • Future trends and challenges in ML applications in biomedical sciences.

We encourage submissions from researchers, clinicians, and data scientists working at the intersection of machine learning and biomedical sciences. This Special Issue aims to provide insights into how ML is shaping the future of biomedicine and improving patient outcomes.

Dr. Matteo Bodini
Dr. Giovanni Cugliari
Dr. Andrea Loddo
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 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. Applied Sciences 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 2400 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

  • machine learning
  • biomedical sciences
  • medical imaging
  • predictive analytics
  • genomics
  • personalized medicine
  • natural language processing (NLP)
  • electronic health records (EHRs)
  • drug discovery
  • explainable AI

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Published Papers (6 papers)

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Research

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19 pages, 1818 KiB  
Article
Explainable AI Highlights the Most Relevant Gait Features for Neurodegenerative Disease Classification
by Gianmarco Tiddia, Francesca Mainas, Alessandra Retico and Piernicola Oliva
Appl. Sci. 2025, 15(14), 8078; https://doi.org/10.3390/app15148078 - 21 Jul 2025
Abstract
Gait analysis is a valuable tool for aiding in the diagnosis of neurological diseases, providing objective measurements of human gait kinematics and kinetics. These data enable the quantitative estimation of movement abnormalities, which helps to diagnose disorders and assess their severity. In this [...] Read more.
Gait analysis is a valuable tool for aiding in the diagnosis of neurological diseases, providing objective measurements of human gait kinematics and kinetics. These data enable the quantitative estimation of movement abnormalities, which helps to diagnose disorders and assess their severity. In this regard, machine learning techniques and explainability methods offer an opportunity to enhance anomaly detection in gait measurements and support a more objective assessment of neurodegenerative disease, providing insights into the most relevant gait parameters used for disease identification. This study employs several classifiers and explainability methods to analyze gait data from a public dataset composed of patients affected by degenerative neurological diseases and healthy controls. The work investigates the relevance of spatial, temporal, and kinematic gait parameters in distinguishing such diseases. The findings are consistent among the classifiers employed and in agreement with known clinical findings about the major gait impairments for each disease. This work promotes the use of data-driven assessments in clinical settings, helping reduce subjectivity in gait evaluation and enabling broader deployment in healthcare environments. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Sciences)
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34 pages, 4523 KiB  
Article
Evaluating Prediction Performance: A Simulation Study Comparing Penalized and Classical Variable Selection Methods in Low-Dimensional Data
by Edwin Kipruto and Willi Sauerbrei
Appl. Sci. 2025, 15(13), 7443; https://doi.org/10.3390/app15137443 - 2 Jul 2025
Viewed by 347
Abstract
Variable selection is important for developing accurate and interpretable prediction models. While classical and penalized methods are widely used, few simulation studies provide meaningful comparisons. This study compares their predictive performance and model complexity in low-dimensional data. Three classical methods (best subset selection, [...] Read more.
Variable selection is important for developing accurate and interpretable prediction models. While classical and penalized methods are widely used, few simulation studies provide meaningful comparisons. This study compares their predictive performance and model complexity in low-dimensional data. Three classical methods (best subset selection, backward elimination, and forward selection) and four penalized methods (nonnegative garrote (NNG), lasso, adaptive lasso (ALASSO), and relaxed lasso (RLASSO)) were compared. Tuning parameters were selected using cross-validation (CV), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Classical methods performed similarly and produced worse predictions than penalized methods in limited-information scenarios (small samples, high correlation, and low signal-to-noise ratio (SNR)), but performed comparably or better in sufficient-information scenarios (large samples, low correlation, and high SNR). Lasso was superior under limited information but was less effective in sufficient-information scenarios. NNG, ALASSO, and RLASSO outperformed lasso in sufficient-information scenarios, with no clear winner among them. AIC and CV produced similar results and outperformed BIC, except in sufficient-information settings, where BIC performed better. Our findings suggest that no single method consistently outperforms others, as performance depends on the amount of information in the data. Lasso is preferred in limited-information settings, whereas classical methods are more suitable in sufficient-information settings, as they also tend to select simpler models. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Sciences)
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12 pages, 489 KiB  
Article
Generative Artificial Intelligence and Risk Appetite in Medical Decisions in Rheumatoid Arthritis
by Florian Berghea, Dan Andras and Elena Camelia Berghea
Appl. Sci. 2025, 15(10), 5700; https://doi.org/10.3390/app15105700 - 20 May 2025
Viewed by 663
Abstract
With Generative AI (GenAI) entering medicine, understanding its decision-making under uncertainty is important. It is well known that human subjective risk appetite influences medical decisions. This study investigated whether the risk appetite of GenAI can be evaluated and if established human risk assessment [...] Read more.
With Generative AI (GenAI) entering medicine, understanding its decision-making under uncertainty is important. It is well known that human subjective risk appetite influences medical decisions. This study investigated whether the risk appetite of GenAI can be evaluated and if established human risk assessment tools are applicable for this purpose in a medical context. Five GenAI systems (ChatGPT 4.5, Gemini 2.0, Qwen 2.5 MAX, DeepSeek-V3, and Perplexity) were evaluated using Rheumatoid Arthritis (RA) clinical scenarios. We employed two methods adapted from human risk assessment: the General Risk Propensity Scale (GRiPS) and the Time Trade-Off (TTO) technique. Queries involving RA cases with varying prognoses and hypothetical treatment choices were posed repeatedly to assess risk profiles and response consistency. All GenAIs consistently identified the same RA cases for the best and worst prognoses. However, the two risk assessment methodologies yielded varied results. The adapted GRiPS showed significant differences in general risk propensity among GenAIs (ChatGPT being the least risk-averse and Qwen/DeepSeek the most), though these differences diminished in specific prognostic contexts. Conversely, the TTO method indicated a strong general risk aversion (unwillingness to trade lifespan for pain relief) across systems yet revealed Perplexity as significantly more risk-tolerant than Gemini. The variability in risk profiles obtained using the GRiPS versus the TTO for the same AI systems raises questions about tool applicability. This discrepancy suggests that these human-centric instruments may not adequately or consistently capture the nuances of risk processing in Artificial Intelligence. The findings imply that current tools might be insufficient, highlighting the need for methodologies specifically tailored for evaluating AI decision-making under medical uncertainty. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Sciences)
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29 pages, 73880 KiB  
Article
Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
by Soohyun Wang, Byoungkug Kim and Doo-Seop Eom
Appl. Sci. 2025, 15(9), 5165; https://doi.org/10.3390/app15095165 - 6 May 2025
Viewed by 544
Abstract
Segmentation of the Optic Disc (OD) and Optic Cup (OC) boundaries in fundus images is a critical step for early glaucoma diagnosis, but accurate segmentation is challenging due to low boundary contrast and significant anatomical variability. To address these challenges, this study proposes [...] Read more.
Segmentation of the Optic Disc (OD) and Optic Cup (OC) boundaries in fundus images is a critical step for early glaucoma diagnosis, but accurate segmentation is challenging due to low boundary contrast and significant anatomical variability. To address these challenges, this study proposes a novel segmentation framework that integrates structure-preserving data augmentation, Boundary-aware Transformer Attention (BAT), and Geometry-aware Loss. We enhance data diversity while preserving vascular and tissue structures through truncated Gaussian-based sampling and colormap transformations. BAT strengthens boundary recognition by globally learning the inclusion relationship between the OD and OC within the skip connection paths of U-Net. Additionally, Geometry-aware Loss, which combines the normalized Hausdorff Distance with the Dice Loss, reduces fine-grained boundary errors and improves boundary precision. The proposed model outperforms existing state-of-the-art models across five public datasets—DRIONS-DB, Drishti-GS, REFUGE, G1020, and ORIGA—and achieves Dice scores of 0.9127 on Drishti-GS and 0.9014 on REFUGE for OC segmentation. For joint segmentation of the OD and OC, it attains high Dice scores of 0.9892 on REFUGE, 0.9782 on G1020, and 0.9879 on ORIGA. Ablation studies validate the independent contributions of each component and demonstrate their synergistic effect when combined. Furthermore, the proposed model more accurately captures the relative size and spatial alignment of the OD and OC and produces smooth and consistent boundary predictions in clinically significant regions such as the region of interest (ROI). These results support the clinical applicability of the proposed method in medical image analysis tasks requiring precise, boundary-focused segmentation. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Sciences)
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Review

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44 pages, 2807 KiB  
Review
Artificial Intelligence in Dermatology: A Review of Methods, Clinical Applications, and Perspectives
by Agnieszka M. Zbrzezny and Tomasz Krzywicki
Appl. Sci. 2025, 15(14), 7856; https://doi.org/10.3390/app15147856 - 14 Jul 2025
Viewed by 384
Abstract
The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models [...] Read more.
The use of artificial intelligence (AI) in dermatology is skyrocketing, but a comprehensive overview integrating regulatory, ethical, validation, and clinical issues is lacking. This work aims to review current research, map applicable legal regulations, identify ethical challenges and methods of verifying AI models in dermatology, assess publication trends, compare the most popular neural network architectures and datasets, and identify good practices in creating AI-based applications for dermatological use. A systematic literature review is conducted in accordance with the PRISMA guidelines, utilising Google Scholar, PubMed, Scopus, and Web of Science and employing bibliometric analysis. Since 2016, there has been exponential growth in deep learning research in dermatology, revealing gaps in EU and US regulations and significant differences in model performance across different datasets. The decision-making process in clinical dermatology is analysed, focusing on how AI is augmenting skin imaging techniques such as dermatoscopy and histology. Further demonstration is provided regarding how AI is a valuable tool that supports dermatologists by automatically analysing skin images, enabling faster diagnosis and the more accurate identification of skin lesions. These advances enhance the precision and efficiency of dermatological care, showcasing the potential of AI to revolutionise the speed of diagnosis in modern dermatology, sparking excitement and curiosity. Then, we discuss the regulatory framework for AI in medicine, as well as the ethical issues that may arise. Additionally, this article addresses the critical challenge of ensuring the safety and trustworthiness of AI in dermatology, presenting classic examples of safety issues that can arise during its implementation. The review provides recommendations for regulatory harmonisation, the standardisation of validation metrics, and further research on data explainability and representativeness, which can accelerate the safe implementation of AI in dermatological practice. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Sciences)
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29 pages, 2411 KiB  
Review
From Genomics to AI: Revolutionizing Precision Medicine in Oncology
by Giulia Calvino, Juliette Farro, Stefania Zampatti, Cristina Peconi, Domenica Megalizzi, Giulia Trastulli, Sarah Andreucci, Raffaella Cascella, Claudia Strafella, Carlo Caltagirone, Federico Grifalchi and Emiliano Giardina
Appl. Sci. 2025, 15(12), 6578; https://doi.org/10.3390/app15126578 - 11 Jun 2025
Viewed by 1277
Abstract
The increasing burden of cancer globally necessitates innovative approaches for diagnosis, prognosis, and treatment. This article explores the transformative impact of genomics and artificial intelligence (AI) in precision oncology, addressing how their convergence is reshaping cancer care and its challenges. Methods: This review [...] Read more.
The increasing burden of cancer globally necessitates innovative approaches for diagnosis, prognosis, and treatment. This article explores the transformative impact of genomics and artificial intelligence (AI) in precision oncology, addressing how their convergence is reshaping cancer care and its challenges. Methods: This review synthesizes current research on the applications of genomics, including next-generation sequencing, and AI, such as machine learning and deep learning, across the cancer care continuum. It examines their roles in identifying genetic variants, assessing cancer risk, guiding targeted therapies and immunotherapy, predicting treatment response, and enabling early detection through liquid biopsies. Results: Genomics and AI are revolutionizing oncology by enabling personalized treatment strategies, improving early detection, and overcoming drug resistance. AI enhances the interpretation of complex genomic data, facilitates drug repurposing, and accelerates the development of novel therapeutics. However, challenges remain regarding data standardization, interpretability, bias in AI algorithms, and ethical considerations. Conclusions: The integration of genomics and AI holds immense potential to advance precision oncology, offering more effective, equitable, and sustainable cancer care. Addressing current challenges and fostering interdisciplinary training will be crucial to fully harness these technologies and redefine oncology practice. Full article
(This article belongs to the Special Issue Machine Learning in Biomedical Sciences)
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