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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: closed (31 December 2025) | Viewed by 19285

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Dr. Md Mohaimenul Islam

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Guest Editor

College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
Interests: artificial intelligence; health informatics, big data; health equity; medication adherence
Special Issues, Collections and Topics in MDPI journals

Dr. Ming-Chin Lin

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Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
Interests: artificial intelligence; machine learning; deep learning; electronic health record; patient safety
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is revolutionizing clinical decision making by offering advanced algorithms and machine learning techniques to analyze vast amounts of medical data and assist healthcare professionals in making accurate and timely diagnoses. AI systems can integrate various types of data, including patient medical history, imaging findings, and genomic information, to provide personalized and evidence-based recommendations. By leveraging AI for clinical decision making, healthcare providers can improve diagnostic accuracy, streamline workflows, and enhance patient outcomes. In this Special Issue, we aim to highlight the latest advancements and innovations in this rapidly evolving field. We invite submissions presenting original research and review articles that explore the application of AI models in diagnosing medical conditions, refining clinical decision-making processes, and ultimately improving patient care outcomes. This Special Issue will serve as a platform to showcase groundbreaking research and foster dialogue among researchers, clinicians, and healthcare policymakers.

Dr. Md Mohaimenul Islam
Dr. Ming-Chin Lin
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.

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Keywords

artificial intelligence
machine learning
clinical decision support systems
health disparity
medical imaging
disease diagnosis
medical errors
patient safety

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Published Papers (7 papers)

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Research

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22 pages, 956 KB

Open AccessArticle

Diagnostic Gap in Rural Maternal Health: Initial Validation of a Parsimonious Clinical Model for Hypertensive Disorders of Pregnancy in a Honduran Hospital

by Isaac Zablah, Carlos Agudelo-Santos, Yolly Molina, Marcio Madrid, Arnoldo Zelaya, Edil Argueta, Salvador Diaz and Antonio Garcia-Loureiro

Diagnostics 2026, 16(1), 132; https://doi.org/10.3390/diagnostics16010132 - 1 Jan 2026

Viewed by 396

Abstract

Background/Objectives: In low-resource settings, diagnostic delays and limited specialist access worsen health inequalities, making hypertensive disorders of pregnancy (HDPs) defined by new-onset blood pressure ≥ 140/90 mmHg after 20 weeks of gestation, with or without proteinuria, a major cause of maternal morbidity and mortality. This study evaluated the diagnostic effectiveness of a rural-applicable clinical model for detecting HDPs in a real-world population from Hospital General San Felipe (Tegucigalpa, Honduras). Methods: A cross-sectional diagnostic accuracy study was conducted on 147 consecutive pregnant women in February 2025. Clinical documentation from the initial appointment defined HDP. We modeled HDP risk using penalized logistic regression and common factors such maternal age, gestational age, blood pressure, BMI, primary symptoms, semi-quantitative proteinuria, and medical history. Median imputation was utilized for missing numbers and stratified five-fold cross-validation assessed performance. We assessed AUROC, AUPRC, Brier score, calibration, and operational utility at a data-driven threshold. Results: Of patients, 27.9% (41/147) had HDP. The model had an AUROC of 0.614, AUPRC of 0.461 (cross-validation averages), and Brier score of 0.253. The threshold with the highest F1-score (0.474) had a sensitivity of 0.561, specificity of 0.679, positive predictive value of 0.404, and negative predictive value of 0.800. HDP had higher meaning systolic/diastolic/mean arterial pressure (130.7/82.9/98.9 vs. 120.5/76.1/90.9 mmHg) and ordinal proteinuria (0.59 vs. 0.36 units). Conclusions: The model had moderate but clinically meaningful discriminative performance using low-cost, commonly obtained variables, excellent calibration, and a good negative predictive value for first exclusion. These findings suggest modification of predictors, a larger sample size, and clinical usefulness assessment using decision curves and process outcomes, including quick referral and prophylaxis. This approach aligns with contemporary developments in the 2023–2025 European Society of Cardiology (ESC) and 2024 American Heart Association (AHA) guidelines, which emphasize earlier identification and risk-stratified management of hypertensive disorders during pregnancy as a cornerstone of women’s cardiovascular health. Full article

(This article belongs to the Special Issue Artificial Intelligence for Clinical Diagnostic Decision Making)

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13 pages, 4226 KB

Open AccessArticle

Multi-Center Validation of Artificial Intelligence-Based Video Analysis Platform for Automatic Evaluation of Swallowing Disorders

by Chang-Won Jeong, Dong-Wook Lim, Si-Hyeong Noh, Hee-Kyung Moon, Chul Park, Nayeon Ko and Min-Su Kim

Diagnostics 2026, 16(1), 45; https://doi.org/10.3390/diagnostics16010045 - 23 Dec 2025

Viewed by 529

Abstract

Background: Videofluoroscopic swallow study (VFSS) is a key examination for assessing swallowing function. Although several artificial intelligence (AI) models for VFSS interpretation have shown high predictive accuracy through internal validations, AI models that have undergone external validation are rare. This study aims to develop an AI model that automatically diagnoses aspiration and penetration from VFSS videos and to evaluate the model’s performance through multicenter external validation. Methods: Among the 2343 VFSS videos collected, 309 cases of Q1-grade videos, which were free of artifacts and clearly showed the airway and vocal cords, were included in the internal validation dataset. The training, internal validation, and test datasets were divided in a 7:1:2 ratio, with 2012 images (aspiration = 532, penetration = 932, no airway invasion = 548) used for training. The AI model was developed and trained using You Only Look Once version 9, model c (YOLOv9_c). External validation of the AI model was conducted using 138 Q1 and Q2-grade VFSS videos from two different hospitals. Results: According to the internal validation, the YOLOv9_c model showed a training accuracy of 98.1%, a validation accuracy of 97.8%, and a test accuracy of 61.5%. From the confusion matrix analysis, the AI model’s diagnostic accuracy for aspiration in VFSS videos was 0.76 (AUC = 0.70), and for penetration, the diagnostic accuracy was 0.66 (AUC = 0.65). According to the external validation, the AI model demonstrated good performance in diagnosing aspiration (precision: 90.2%, AUC = 0.79) and penetration (precision: 78.3%, AUC = 0.80). The overall diagnostic accuracy of external validation for VFSS videos was 80.4%. Conclusions: We developed an AI model that automatically diagnoses aspiration and penetration when an entire VFSS video is input, and external validation showed good accuracy. In the future, to improve the performance of this AI model and facilitate its clinical application, research using training and validation with VFSS video data from more hospitals is needed. Full article

(This article belongs to the Special Issue Artificial Intelligence for Clinical Diagnostic Decision Making)

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16 pages, 1286 KB

Open AccessArticle

Integrating Feature Selection, Machine Learning, and SHAP Explainability to Predict Severe Acute Pancreatitis

by İzzet Ustaalioğlu and Rohat Ak

Diagnostics 2025, 15(19), 2473; https://doi.org/10.3390/diagnostics15192473 - 27 Sep 2025

Viewed by 1221

Abstract

Background/Objectives: Severe acute pancreatitis (SAP) carries substantial morbidity and resource burden, and early risk stratification remains challenging with conventional scores that require serial observations. The aim of this study was to develop and compare supervised machine-learning (ML) pipelines—integrating feature selection and SHAP-based explainability—for early prediction of SAP at emergency department (ED) presentation. Methods: This retrospective, single-center cohort was conducted in a tertiary-care ED between 1 January 2022 and 1 January 2025. Adult patients with acute pancreatitis were identified from electronic records; SAP was classified per the Revised Atlanta criteria (persistent organ failure ≥ 48 h). Six feature-selection methods (univariate AUROC filter, RFE, mRMR, LASSO, elastic net, Boruta) were paired with six classifiers (kNN, elastic-net logistic regression, MARS, random forest, SVM-RBF, XGBoost) to yield 36 pipelines. Discrimination, calibration, and error metrics were estimated with bootstrapping; SHAP was used for model interpretability. Results: Of 743 patients (non-SAP 676; SAP 67), SAP prevalence was 9.0%. Compared with non-SAP, SAP patients more often had hypertension (38.8% vs. 27.1%) and malignancy (19.4% vs. 7.2%); they presented with lower GCS, higher heart and respiratory rates, lower systolic blood pressure, and more frequent peripancreatic fluid (31.3% vs. 16.9%) and pleural effusion (43.3% vs. 17.5%). Albumin was lower by 4.18 g/L, with broader renal–electrolyte and inflammatory derangements. Across the best-performing models, AUROC spanned 0.750–0.826; the top pipeline (RFE–RF features + kNN) reached 0.826, while random-forest-based pipelines showed favorable calibration. SHAP confirmed clinically plausible contributions from routinely available variables. Conclusions: In this study, integrating feature selection with ML produced accurate and interpretable early prediction of SAP using data available at ED arrival. The approach highlights actionable predictors and may support earlier triage and resource allocation; external validation is warranted. Full article

(This article belongs to the Special Issue Artificial Intelligence for Clinical Diagnostic Decision Making)

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34 pages, 2988 KB

Open AccessArticle

Improving Surgical Site Infection Prediction Using Machine Learning: Addressing Challenges of Highly Imbalanced Data

by Salha Al-Ahmari and Farrukh Nadeem

Diagnostics 2025, 15(4), 501; https://doi.org/10.3390/diagnostics15040501 - 19 Feb 2025

Cited by 5 | Viewed by 2051

Abstract

Background: Surgical site infections (SSIs) lead to higher hospital readmission rates and healthcare costs, representing a significant global healthcare burden. Machine learning (ML) has demonstrated potential in predicting SSIs; however, the challenge of addressing imbalanced class ratios remains. Objectives: The aim of this study is to evaluate and enhance the predictive capabilities of machine learning models for SSIs by assessing the effects of feature selection, resampling techniques, and hyperparameter optimization. Methods: Using routine SSI surveillance data from multiple hospitals in Saudi Arabia, we analyzed a dataset of 64,793 surgical patients, of whom 1632 developed SSI. Seven machine learning algorithms were created and tested: Decision Tree (DT), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Stochastic Gradient Boosting (SGB), and K-Nearest Neighbors (KNN). We also improved several resampling strategies, such as undersampling and oversampling. Grid search five-fold cross-validation was employed for comprehensive hyperparameter optimization, in conjunction with balanced sampling techniques. Features were selected using a filter method based on their relationships with the target variable. Results: Our findings revealed that RF achieves the highest performance, with an MCC of 0.72. The synthetic minority oversampling technique (SMOTE) is the best-performing resampling technique, consistently enhancing the performance of most machine learning models, except for LR and GNB. LR struggles with class imbalance due to its linear assumptions and bias toward the majority class, while GNB’s reliance on feature independence and Gaussian distribution make it unreliable for under-represented minority classes. For computational efficiency, the Instance Hardness Threshold (IHT) offers a viable alternative undersampling technique, though it may compromise performance to some extent. Conclusions: This study underscores the potential of ML models as effective tools for assessing SSI risk, warranting further clinical exploration to improve patient outcomes. By employing advanced ML techniques and robust validation methods, these models demonstrate promising accuracy and reliability in predicting SSI events, even in the face of significant class imbalances. In addition, using MCC in this study ensures a more reliable and robust evaluation of the model’s predictive performance, particularly in the presence of an imbalanced dataset, where other metrics may fail to provide an accurate evaluation. Full article

(This article belongs to the Special Issue Artificial Intelligence for Clinical Diagnostic Decision Making)

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Review

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24 pages, 450 KB

Open AccessReview

A Review of the Use of Data Analytics to Address Preeclampsia in Ecuador Between 2020 and 2024

by Franklin Parrales-Bravo, Lorenzo Cevallos-Torres, Leonel Vasquez-Cevallos, Rosangela Caicedo-Quiroz, Roberto Tolozano-Benites and Víctor Gómez-Rodríguez

Diagnostics 2025, 15(8), 978; https://doi.org/10.3390/diagnostics15080978 - 11 Apr 2025

Cited by 4 | Viewed by 1946

Abstract

Preeclampsia is one of the leading causes of maternal and perinatal morbidity and mortality worldwide. The incidence of preeclampsia in Ecuador is approximately 51 cases per 1000 pregnancies. Despite advances in medicine, its diagnosis and management remain a challenge due to its multifactorial nature and variability in its clinical presentation. Data analytics offers an innovative approach to address these challenges, allowing for better understanding of the disease and more informed decision-making. This work review examines peer-reviewed studies published during the last decade that employed descriptive, diagnostic, predictive, and prescriptive analytics to evaluate preeclampsia in Ecuador. The review focuses on studies conducted in healthcare institutions across coastal and highland regions, with an inclusion criterion requiring sample sizes greater than 100 patients. Emphasis is placed on the statistical methods used, main findings, and the technological capabilities of the facilities where the analyses were performed. Critical evaluation of methodology limitations and a comparative discussion of findings with global literature on preeclampsia are included. The synthesis of these studies highlights both progress and gaps in predictive analytics for preeclampsia and suggests pathways for future research. Full article

(This article belongs to the Special Issue Artificial Intelligence for Clinical Diagnostic Decision Making)

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31 pages, 1981 KB

Open AccessReview

Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications

by Vineet Vinay, Praveen Jodalli, Mahesh S. Chavan, Chaitanya. S. Buddhikot, Alexander Maniangat Luke, Mohamed Saleh Hamad Ingafou, Rodolfo Reda, Ajinkya M. Pawar and Luca Testarelli

Diagnostics 2025, 15(3), 280; https://doi.org/10.3390/diagnostics15030280 - 24 Jan 2025

Cited by 22 | Viewed by 8402

Abstract

Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral cancer diagnosis applications to address a gap. Methods: A scoping review identified, selected, and synthesized AI-based oral cancer diagnosis, screening, and prognosis literature. The review verified study quality and relevance using frameworks and inclusion criteria. A full search included keywords, MeSH phrases, and Pubmed. Oral cancer AI applications were tested through data extraction and synthesis. Results: AI outperforms traditional oral cancer screening, analysis, and prediction approaches. Medical pictures can be used to diagnose oral cancer with convolutional neural networks. Smartphone and AI-enabled telemedicine make screening affordable and accessible in resource-constrained areas. AI methods predict oral cancer risk using patient data. AI can also arrange treatment using histopathology images and address data heterogeneity, restricted longitudinal research, clinical practice inclusion, and ethical and legal difficulties. Future potential includes uniform standards, long-term investigations, ethical and regulatory frameworks, and healthcare professional training. Conclusions: AI may transform oral cancer diagnosis and treatment. It can develop early detection, risk modelling, imaging phenotypic change, and prognosis. AI approaches should be standardized, tested longitudinally, and ethical and practical issues related to real-world deployment should be addressed. Full article

(This article belongs to the Special Issue Artificial Intelligence for Clinical Diagnostic Decision Making)

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Other

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13 pages, 639 KB

Open AccessSystematic Review

Machine Learning Models for Predicting Mortality in Critically Ill Patients with Sepsis-Associated Acute Kidney Injury: A Systematic Review

by Chieh-Chen Wu, Tahmina Nasrin Poly, Yung-Ching Weng, Ming-Chin Lin and Md. Mohaimenul Islam

Diagnostics 2024, 14(15), 1594; https://doi.org/10.3390/diagnostics14151594 - 24 Jul 2024

Cited by 4 | Viewed by 2963

Abstract

While machine learning (ML) models hold promise for enhancing the management of acute kidney injury (AKI) in sepsis patients, creating models that are equitable and unbiased is crucial for accurate patient stratification and timely interventions. This study aimed to systematically summarize existing evidence to determine the effectiveness of ML algorithms for predicting mortality in patients with sepsis-associated AKI. An exhaustive literature search was conducted across several electronic databases, including PubMed, Scopus, and Web of Science, employing specific search terms. This review included studies published from 1 January 2000 to 1 February 2024. Studies were included if they reported on the use of ML for predicting mortality in patients with sepsis-associated AKI. Studies not written in English or with insufficient data were excluded. Data extraction and quality assessment were performed independently by two reviewers. Five studies were included in the final analysis, reporting a male predominance (>50%) among patients with sepsis-associated AKI. Limited data on race and ethnicity were available across the studies, with White patients comprising the majority of the study cohorts. The predictive models demonstrated varying levels of performance, with area under the receiver operating characteristic curve (AUROC) values ranging from 0.60 to 0.87. Algorithms such as extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR) showed the best performance in terms of accuracy. The findings of this study show that ML models hold immense ability to identify high-risk patients, predict the progression of AKI early, and improve survival rates. However, the lack of fairness in ML models for predicting mortality in critically ill patients with sepsis-associated AKI could perpetuate existing healthcare disparities. Therefore, it is crucial to develop trustworthy ML models to ensure their widespread adoption and reliance by both healthcare professionals and patients. Full article

(This article belongs to the Special Issue Artificial Intelligence for Clinical Diagnostic Decision Making)

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