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Application of Artificial Intelligence in Disease Diagnosis and Treatment

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A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Epidemiology & Public Health".

Deadline for manuscript submissions: 25 September 2026 | Viewed by 5902

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Special Issue Editors

Prof. Dr. Ricardo Martinez-Murillo

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Guest Editor

Neurovascular Research Group, Instituto Cajal (CSIC), Avenue Doctor Arce 37, 28002 Madrid, Spain
Interests: the neurovascular unit in ageing; ischemic stroke; Alzheimer's disease; exploring protective mechanisms and therapeutic molecules such as nitrones and peptide mimetics

Dr. Oscar Casanova Carvajal

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Guest Editor Assistant

Laboratorio de Bioinstrumentación y Nanomedicina, Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: AI-driven nanomedicine for cancer therapy; biomedical signal and image processing; smart electronic instrumentation; technology-enhanced education in health and engineering

Special Issue Information

Dear Colleagues,

The integration of Artificial Intelligence (AI) into healthcare systems is profoundly transforming the landscape of disease diagnosis and treatment. This Special Issue aims to collate pioneering contributions that illustrate how AI-driven methodologies, including machine learning, deep learning, and natural language processing, are enhancing diagnostic accuracy, facilitating early disease detection, and enabling personalised treatment planning across a wide range of medical domains. Topics of interest include the application of AI in radiology and pathology through image analysis, the development of predictive models for patient risk stratification, the implementation of clinical decision support systems, and the use of AI in genomics and drug discovery. Contributions addressing challenges such as data quality, interpretability, bias mitigation, and clinical validation are also welcomed. Submissions featuring novel algorithms, validated clinical applications, interdisciplinary approaches, and ethical reflections on the deployment of AI in real-world medical settings are particularly encouraged, with the overarching aim of highlighting the transformative potential of AI in improving patient outcomes and reshaping the future of modern medicine.

The application of Artificial Intelligence (AI) in healthcare has evolved significantly over the past two decades. Initially focused on decision-tree logic and basic automation, AI technologies have rapidly advanced with the rise of machine learning and deep neural networks. In medical imaging, natural language processing, and precision medicine, AI has already demonstrated tangible improvements in diagnostic speed, reproducibility, and sensitivity. The COVID-19 pandemic further accelerated the adoption of AI tools in triage, epidemiological modelling, and treatment optimisation, underscoring the urgency of integrating such technologies into routine clinical workflows.

We are particularly interested in research that applies advanced AI methods—including convolutional neural networks, generative models, reinforcement learning, and explainable AI—to complex clinical challenges. Emerging topics such as federated learning, AI-integrated multiomics, clinical prediction models, and real-time decision support systems are especially welcome. Additionally, we value research that rigorously evaluates AI performance in clinical trials, benchmarks algorithms against traditional methods, or explores the ethical and regulatory dimensions of AI in medicine.

We welcome original research articles and systematic reviews that address the application of AI in medical diagnosis and treatment. Submissions should include a strong methodological foundation, clear clinical relevance, and a focus on innovation. Interdisciplinary work combining computer science, biomedical engineering, and clinical medicine is highly encouraged. Papers offering novel datasets, open-source tools, or clinically validated systems are especially valued. We also invite perspective pieces on the integration of AI into healthcare systems and the future challenges in regulation, interpretability, and patient trust.

Prof. Dr. Ricardo Martinez-Murillo
Guest Editor

Dr. Oscar Casanova Carvajal
Guest Editor Assistant

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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. Medicina is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

artificial intelligence
machine learning
deep learning
medical diagnosis
clinical decision support
predictive modelling
medical imaging
personalised medicine
healthcare AI applications
explainable AI

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Published Papers (5 papers)

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Research

14 pages, 1001 KB

Open AccessArticle

Artificial Intelligence-Derived Electrocardiogram Analysis for Identification of Carbon Monoxide-Induced Cardiomyopathy: A Retrospective Study

by Heewon Yang, Moon-Seung Soh, Min Sung Lee, Sungwoo Choi, Sangsoo Han, Sung-Eun Lee, Yura Ko and Sangchun Choi

Medicina 2026, 62(6), 1081; https://doi.org/10.3390/medicina62061081 - 2 Jun 2026

Viewed by 177

Abstract

Background and Objectives: The diagnostic accuracy of an artificial intelligence (AI)-derived initial 12-lead electrocardiogram (ECG) analysis was evaluated for early carbon monoxide-induced cardiomyopathy (CO-CMP) risk detection. Materials and Methods: Retrospective medical data of carbon monoxide poisoning (COP) cases between 1 January 2015 and 31 December 2024 were screened for the primary outcome: odds ratio (OR) for echocardiographically confirmed CO-CMP among those with high-risk probability score per the AI-derived model. Secondary outcomes included left ventricular ejection fraction (LVEF) and AI-derived probability score, critical care requirements, including intubation and intensive care unit (ICU) admission, and cardiac arrest events. Results: A total of 51 patients with acute COP were included in the final analysis, with 13 (25.5%) being diagnosed with CO-CMP. The LVEF in the CO-CMP group was lower than that in the non-CO-CMP group (40.00 ± 13.80% vs. 63.76 ± 6.24%, p < 0.001). The AI-derived probability score was higher in the CO-CMP group (11.3 [3.8–32.7] vs. 0.5 [0.2–2.2], p < 0.001). Among cardiac biomarkers, troponin I (2.37 [0.32–7.88] vs. 0.06 [0.06–0.95] ng/mL, p = 0.002) was higher in the CO-CMP group. Patients with CO-CMP required recurrent ventilator support (76.9% vs. 21.1%, p < 0.001) and ICU admission (92.3% vs. 42.1%, p = 0.003). In multivariable regression analysis, the AI-derived prediction model was independently associated with CO-CMP (OR 1.14; 95% confidence interval (CI) 1.02–1.27; p = 0.017; Firth-penalized OR 1.11; 95% CI 1.03–1.25; p < 0.001). Receiver operating characteristic analysis of the AI-derived model showed an area under the curve of 0.85 (95% CI 0.70–0.96) for the AI score alone and 0.92 (95% CI 0.83–0.99) for the Combined AI–cardiac marker model, with a sensitivity of 92.3% and specificity of 81.6%. Pairwise DeLong comparisons between the Combined AI model and comparator models did not reach statistical significance (Combined vs. AI-only, p = 0.092; Combined vs. cardiac markers, p = 0.052); however, the likelihood-ratio test for adding the AI probability score to the cardiac marker-only model demonstrated significant incremental information (χ² = 13.68, p < 0.001). Conclusions: AI-based ECG analysis showed exploratory diagnostic association with LV systolic dysfunction observed in suspected CO-CMP patients. Given the limited sample size, low events-per-variable ratio, and lack of external validation, these findings suggest that AI-ECG analysis may provide incremental information for early cardiac risk stratification in selected patients. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Disease Diagnosis and Treatment)

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25 pages, 3685 KB

Open AccessArticle

Explainable Meta-Learning Ensemble Framework for Predicting Insulin Dose Adjustments in Diabetic Patients: A Comparative Machine Learning Approach with SHAP-Based Clinical Interpretability

by Emek Guldogan, Burak Yagin, Hasan Ucuzal, Abdulmohsen Algarni, Fahaid Al-Hashem and Mohammadreza Aghaei

Medicina 2026, 62(3), 502; https://doi.org/10.3390/medicina62030502 - 9 Mar 2026

Viewed by 921

Abstract

Background and Objectives: Diabetes mellitus represents one of the most prevalent chronic metabolic disorders worldwide, necessitating precise insulin dose management to prevent both acute and long-term complications. The optimization of insulin dosing remains a significant clinical challenge, as inappropriate dosing can lead to hypoglycemia or hyperglycemia, each carrying substantial morbidity risks. Machine learning approaches have emerged as promising tools for developing clinical decision support systems; however, their practical implementation requires both high predictive accuracy and model interpretability. This study aimed to develop and evaluate an explainable machine learning framework for predicting insulin dose adjustments in diabetic patients. We sought to compare multiple ensemble learning approaches and identify the optimal model configuration that balances predictive performance with clinical interpretability through comprehensive SHAP and LIME analyses. Materials and Methods: A comprehensive dataset comprising 10,000 patient records with 12 clinical and demographic features was utilized. We implemented and compared nine machine learning models, including gradient boosting variants (XGBoost, LightGBM, CatBoost, GradientBoosting), AdaBoost, and four ensemble strategies (Voting, Stacking, Blending, and Meta-Learning). Model interpretability was achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analyses. Performance was evaluated using accuracy, weighted F1-score, area under the receiver operating characteristic curve (AUC-ROC), precision-recall AUC (PR-AUC), sensitivity, specificity, and cross-entropy loss. Results: The Meta-Learning Ensemble achieved superior performance across all evaluation metrics, attaining an accuracy of 81.35%, weighted F1-score of 0.8121, macro-averaged AUC-ROC of 0.9637, and PR-AUC of 0.9317. The model demonstrated exceptional sensitivity (86.61%) and specificity (91.79%), with particularly high performance in detecting dose reduction requirements (100% sensitivity for the ‘down’ class). SHAP analysis revealed insulin sensitivity, previous medications, sleep hours, weight, and body mass index as the most influential predictors across different insulin adjustment categories. The meta-model feature importance analysis indicated that LightGBM probability estimates contributed most significantly to the ensemble predictions. Conclusions: The proposed explainable Meta-Learning Ensemble framework demonstrates robust predictive capability for insulin dose adjustment recommendations while maintaining clinical interpretability. The integration of SHAP-based explanations facilitates clinician understanding of model predictions, supporting transparent and informed decision-making in diabetes management. This approach represents a significant advancement toward the clinical implementation of artificial intelligence in personalized insulin therapy. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Disease Diagnosis and Treatment)

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20 pages, 1913 KB

Open AccessArticle

Development and Internal Evaluation of an Interpretable AI-Based Composite Score for Psychosocial and Behavioral Screening in Dental Clinics Using a Mamdani Fuzzy Inference System

by Alexandra Lavinia Vlad, Florin Sandu Blaga, Ioana Scrobota, Raluca Ortensia Cristina Iurcov, Gabriela Ciavoi, Anca Maria Fratila and Ioan Andrei Țig

Medicina 2026, 62(2), 412; https://doi.org/10.3390/medicina62020412 - 21 Feb 2026

Cited by 1 | Viewed by 703

Abstract

Background and Objectives: Psychosocial symptoms and oral behaviors can complicate routine dental care, yet available screeners yield multiple separate scores. Explainable artificial intelligence offers a pragmatic way to integrate such multidomain measures into a single, auditable output that can support screening-oriented stratification and standardized documentation (non-diagnostic). Therefore, we aimed to develop an interpretable, deterministic Mamdani fuzzy inference system (FIS) integrating GAD-7, PHQ-9, and OBC-21 into a 0–10 psychobehavioral composite score (PCS) to support screening-oriented stratification and standardized documentation (non-diagnostic). Materials and Methods: Cross-sectional multicenter study in 18 private dental clinics in Romania (October 2024–March 2025; n = 460). A rule-based Mamdani Type-1 FIS was specified a priori (48 rules; triangular membership functions; centroid defuzzification) without supervised training. Internal evaluation assessed coherence across severity strata, robustness to predefined input perturbations (±1 point; ±5%) and membership-function variation (±10%), and benchmarking against linear composites (Z-mean; PCA PC1). Results: Median PCS was 2.30 (IQR 2.03–3.56). PCS correlated with GAD-7 (Spearman ρ = 0.886), PHQ-9 (ρ = 0.792), and OBC-21 (ρ = 0.687) (all p < 0.001), increased monotonically across anxiety and depression severity strata, and was higher in high OBC-21 risk. Robustness was excellent under input perturbations (ICC(3,1) = 0.983 for ±1 point; 0.992 for ±5%) and high under ±10% membership-function variation (ICC(3,1) = 0.959). Concordance with linear baselines was high (Spearman ρ = 0.956 for Z-mean; 0.955 for PCA PC1), with a small systematic nonlinearity at higher scores. Conclusions: PCS provides a fully auditable, rule-based integration of three patient-reported measures with coherent internal behavior and robustness to plausible measurement noise and specification changes. This study reports internal evaluation of a deterministic, rule-based aggregation; external clinical validation against independent outcomes is required before any clinical utility claims. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Disease Diagnosis and Treatment)

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14 pages, 1278 KB

Open AccessArticle

Pregnancy AI: Development and Internal Validation of an Artificial Intelligence Tool to Predict Live Births in ICSI and IVF Cycles Using Clinical Features and Embryo Images

by Jaume Minano Masip, Penelope Borduas, Isaac-Jacques Kadoch, Simon Phillips, Doina Precup and Daniel Dufort

Medicina 2026, 62(2), 364; https://doi.org/10.3390/medicina62020364 - 12 Feb 2026

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Abstract

Background and Objectives: This study aimed at developing an AI-based predictive model for live birth based on a combination of a support vector machine (SVM) using clinical and embryological features, together with a convolutional neural network (CNN) using embryo time-lapse videos. Materials and Methods: This was a retrospective cohort analysis. Two hundred fifty-nine infertile couples treated between January 2012 and December 2019, with a total of 2330 embryos, were included in this study, and clinical data and images from 355 transferred embryos were used to build a predictive model. The main outcome was accuracy of live birth prediction. The secondary outcomes included accuracy in the prediction of biochemical pregnancy, clinical pregnancy and transferrable embryos. Results: The model was able to predict the transferrable embryo (i.e., embryos suitable for transfer or cryopreservation) with an accuracy of 0.98 in an internal set. The accuracy for predicting live birth, clinical pregnancy, and biochemical pregnancy exclusively using clinical data as input for an SVM model was 0.67, 0.68, and 0.67, respectively. With six frames from time-lapse embryo development, the CNN produced an accuracy of 0.57, 0.67, and 0.72. The predictive model performed best when combining input from clinical data and images from multiple embryo developmental frames, obtaining 0.71, 0.73, and 0.77 for predicting live birth, clinical pregnancy, and biochemical pregnancy. Conclusions: This study highlights the potential of combining clinical data and embryo development images to enhance predictive models in reproductive medicine. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Disease Diagnosis and Treatment)

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30 pages, 1993 KB

Open AccessArticle

Artificial Intelligence Pipeline for Mammography-Based Breast Cancer Detection: An Integrated Systematic Review and Large-Scale Experimental Validation

by Daniel Añez, Giuseppe Conti, Juan José Uriarte, José-Javier Serrano-Olmedo, Ricardo Martínez-Murillo and Oscar Casanova-Carvajal

Medicina 2025, 61(12), 2237; https://doi.org/10.3390/medicina61122237 - 18 Dec 2025

Cited by 2 | Viewed by 1855

Abstract

Background and Objectives: Breast cancer remains a leading cause of cancer-related morbidity and mortality worldwide, and robust, interpretable artificial intelligence (AI) pipelines are increasingly being explored to support mammography-based detection. This study combines a PRISMA 2020-compliant systematic review with an original experimental validation to characterize current evidence and address identified gaps in reproducibility and interpretability. Materials and Methods: A PRISMA 2020-guided systematic review and an original experimental study were conducted. The review searched PubMed and Scopus/ScienceDirect for studies using convolutional neural networks (CNNs), support vector machines (SVMs) or eXtreme Gradient Boosting (XGBoost) for breast cancer detection in mammography and related imaging modalities, and identified 45 eligible articles. In parallel, we implemented and evaluated representative CNN (ResNet-50, EfficientNetB0 and MobileNetV3-Small) and classical machine learning (SVM and XGBoost) pipelines on the CBIS-DDSM dataset, following a CRISP-DM-inspired workflow and using Grad-CAM and SHAP to provide image- and feature-level explanations within a reproducible machine-learning-operations (MLOps)-oriented framework. Results: The systematic review revealed substantial heterogeneity in datasets, preprocessing pipelines, and validation strategies, with a predominant reliance on internal validation and limited use of explainable AI methods. In our experimental evaluation, ResNet-50 achieved the best performance (AUC-ROC 0.95; sensitivity 89%), followed by XGBoost (AUC-ROC 0.90; sensitivity 74%) and SVM (AUC-ROC 0.84; sensitivity 66%), while EfficientNetB0 and MobileNetV3-Small showed lower discrimination. Grad-CAM produced qualitatively plausible heatmaps centered on annotated lesions, and SHAP analyses indicated that simple global image-intensity and size descriptors dominated the predictions of the classical models. Conclusions: By integrating systematic evidence and large-scale experiments on CBIS-DDSM, this study highlights both the potential and the limitations of current AI pipelines for mammography-based breast cancer detection, underscoring the need for more standardized preprocessing, rigorous external validation, and routine use of explainable AI before clinical deployment. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Disease Diagnosis and Treatment)

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