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Computers
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Artificial Intelligence (AI) in Medical Informatics
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Artificial Intelligence (AI) in Medical Informatics

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2581

Special Issue Editor

Dr. Tiehang Duan

E-Mail Website
Guest Editor
Department of Computer Science, Grand Valley State University, Allendale, MI 49401, USA
Interests: machine learning; brain-computer interfaces; medical informatics

Special Issue Information

Dear Colleagues,

The rapid development of Artificial Intelligence (AI) has profoundly transformed the field of Medical Informatics, enabling more efficient data analysis, improved clinical decision-making, and personalized healthcare solutions. At the same time, explosive growth in digital health data—spanning electronic health records (EHRs), high-resolution medical imaging, multi-omics sequencing, signal data from wearable sensors and Internet of Medical Things (IoMT) devices, and unstructured clinical notes—has created both immense opportunities and complex analytical challenges. AI-driven methods play a crucial role in extracting meaningful insights, accelerating discovery, enhancing diagnostic accuracy, optimizing therapeutic strategies, and enabling truly personalized, predictive, and preventive healthcare.

This Special Issue aims to provide a comprehensive forum for recent advances in AI methodologies and their applications in Medical Informatics. We welcome original research articles and review papers that explore innovative AI techniques and demonstrate novel theoretical contributions, methodological innovations, robust validation on clinical datasets, or successful translational implementations, including but not limited to machine learning, deep learning, natural language processing, data mining, and biomedical informatics, applied to medical data analysis and healthcare systems.

Topics of interest include, but are not limited to the following:

  • AI-based clinical decision support systems;
  • Medical image and signal analysis;
  • Electronic health record (EHR) analytics;
  • Predictive modeling and risk assessment in healthcare;
  • Personalized and precision medicine;
  • AI applications in medical diagnostics and prognosis;
  • Interpretability, explainability, and uncertainty quantification in medical applications;
  • Generative AI, synthetic data generation, and augmentation in healthcare applications
  • Ethical, interpretability, and reliability issues of AI in healthcare;
  • Integration of AI solutions into clinical workflows.

By bringing together interdisciplinary research from computer science, data science, and medicine, this Special Issue seeks to highlight cutting-edge developments, practical challenges, and future directions of AI in Medical Informatics, ultimately contributing to improved healthcare quality and patient outcomes.

Dr. Tiehang Duan
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. Computers 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 1800 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 (AI)
  • medical informatics
  • brain–computer interfaces, machine learning
  • deep learning
  • clinical decision support systems
  • medical data analytics
  • Electronic Health Records (EHRs)
  • medical image analysis
  • Natural Language Processing (NLP)
  • predictive modeling in healthcare
  • precision and personalized medicine
  • healthcare data mining
  • wearable and sensor data
  • brain signal analysis, explainable AI in healthcare
  • EEG decoding
  • ethics and trustworthy AI

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

Published Papers (5 papers)

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Research

16 pages, 361 KB  
Article
On-Device Transformer Architectures for Speech Evaluation in Neurodegenerative Disease Detection
by Lara Marie Reimer, Leonard Pries, Florian Schweizer, Leon Nissen and Stephan M. Jonas
Computers 2026, 15(5), 287; https://doi.org/10.3390/computers15050287 - 1 May 2026
Viewed by 134
Abstract
Speech alterations are early markers of neurodegenerative diseases. Transformer-based speech models such as Whisper have advanced automated speech assessment, but most systems rely on cloud-based computation, raising privacy concerns. On-device processing could offer a scalable and privacy-preserving alternative. This research’s objective was to [...] Read more.
Speech alterations are early markers of neurodegenerative diseases. Transformer-based speech models such as Whisper have advanced automated speech assessment, but most systems rely on cloud-based computation, raising privacy concerns. On-device processing could offer a scalable and privacy-preserving alternative. This research’s objective was to evaluate whether a fully on-device speech analysis pipeline can achieve competitive accuracy in detecting Alzheimer’s disease and to quantify the contributions of acoustic, linguistic, and embedding features. Therefore, we developed an iOS application running all components, including acoustic analysis, two transformer-based speech-to-text modules (WhisperBase and quantized CrisperWhisper), linguistic feature extraction, and embedding generation, directly on the device. Using the ADReSS Challenge 2020 dataset (N = 156), we trained classical machine-learning classifiers across 20 configurations and evaluated them via a stratified 10-fold cross-validation. Area under the receiver operating curve (AUC), accuracy, precision, recall, and F1 scores were used as performance metrics. An ablation study examined the relevance of each feature group. The best-performing setup (Random Forest with CrisperWhisper transcription and Apple embeddings) achieved an accuracy of 85.4% and an AUC of 0.85. Performance was 5–7% below benchmark models relying on manual transcripts or server-based processing. Embedding features provided the strongest individual contribution, but the highest accuracy required combining acoustic, linguistic, and embedding information. A fully on-device pipeline for Alzheimer’s disease detection from speech is feasible and achieves competitive accuracy while maintaining strict data privacy. These findings highlight the potential of on-device transformer architectures for scalable, privacy-preserving digital screening. Future work should validate the approach in larger and more diverse cohorts. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Medical Informatics)
25 pages, 6442 KB  
Article
YOLOv12-WCIRS: An Improved YOLOv12-Based Framework for Small Intestinal Lesion Detection in WCE
by Shiren Ye, Liangjing Li, Zetong Zhang and Haipeng Ma
Computers 2026, 15(5), 283; https://doi.org/10.3390/computers15050283 - 29 Apr 2026
Viewed by 233
Abstract
Accurate detection of small intestinal lesions in wireless capsule endoscopy (WCE) images remains challenging because lesions are often small, weakly contrasted, irregular in shape, and easily confused with complex mucosal backgrounds. To address these difficulties, this study proposes YOLOv12-WCIRS, a WCE-oriented improvement of [...] Read more.
Accurate detection of small intestinal lesions in wireless capsule endoscopy (WCE) images remains challenging because lesions are often small, weakly contrasted, irregular in shape, and easily confused with complex mucosal backgrounds. To address these difficulties, this study proposes YOLOv12-WCIRS, a WCE-oriented improvement of YOLOv12 that jointly enhances local feature extraction, selective multi-scale fusion, background suppression, localization sensitivity, and scale-aware optimization. The proposed framework incorporates a Weighted Convolution (WConv) module, a Contextual Selection Fusion Module (CSFM), an Information Integration Attention Fusion (IIA_Fusion) module, a Receptive Field Attention-based detection head (RFAHeadDetect), and a Scale Dynamic Loss (SD Loss). Experiments on the SEE-AI dataset show that YOLOv12-WCIRS achieves 83.4% mAP@0.5 and 61.1% mAP@0.5:0.95, improving mAP@0.5 from 76.9% to 83.4% over the direct baseline YOLOv12 while maintaining competitive efficiency. Additional analyses, including cross-dataset validation on overlapping categories in Kvasir-Capsule, normal-frame false-alarm evaluation, false-positive/false-negative breakdown, and repeated-run statistical testing, further support the robustness and practical value of the proposed framework. These results indicate that YOLOv12-WCIRS provides an effective solution for automated lesion detection in WCE images and shows promise for computer-aided capsule endoscopy analysis. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Medical Informatics)
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22 pages, 15509 KB  
Article
Colonic Polyp Detection with Object Detection Models
by Raluca Portase and Eugen-Richard Ardelean
Computers 2026, 15(4), 258; https://doi.org/10.3390/computers15040258 - 20 Apr 2026
Viewed by 466
Abstract
In recent years, deep learning has been applied more and more to medical image analysis. One such application of deep learning is the automated polyp detection in colonoscopy with the target of reducing miss rates. This study presents a comprehensive evaluation of nine [...] Read more.
In recent years, deep learning has been applied more and more to medical image analysis. One such application of deep learning is the automated polyp detection in colonoscopy with the target of reducing miss rates. This study presents a comprehensive evaluation of nine state-of-the-art object detection models for colonic polyp detection: YOLOv8, YOLOv9, YOLOv10, YOLO11, YOLO12, YOLO26, RT-DETR, YOLO-World, and YOLOE. The models were evaluated on three publicly available datasets: CVC-ClinicDB, CVC-ColonDB, and ETIS-LaribPolypDB. All models were trained under standardized conditions using identical hyperparameters and data augmentation strategies to guarantee fair comparison. Performance was evaluated using multiple metrics: mAP@50, mAP@50–95, F1 score, precision, recall, inference time, and computational cost. YOLO11 demonstrated the best overall performance, achieving mAP@50 scores of 0.995, 0.944, and 0.978 on the three datasets respectively, while maintaining the fastest inference time of approximately 150 ms per image and the third-lowest computational cost at 21.3 GFLOPs. Cross-dataset generalization experiments revealed a significant loss of performance, with mAP@50 dropping by 20–40% when models were tested on an unseen dataset, highlighting the challenge of true generalization with limited datasets. Statistical analysis by polyp size showed that while all models achieved F1 scores exceeding 0.95 for large polyps, performance decreased to 0.60–0.85 for small polyps, indicating a limitation in detecting small lesions. The analysis of failure modes showed that missed detections, false positives and boundary errors constitute 60–75% of all failures, suggesting that domain adaptation of object detection models may be required. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Medical Informatics)
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37 pages, 4219 KB  
Article
PIRE: Interoperable Platform for Electronic Records
by Leonardo Juan Ramirez Lopez, Norman Eduardo Jaimes Salazar and Juan Esteban Barbosa Posada
Computers 2026, 15(3), 162; https://doi.org/10.3390/computers15030162 - 3 Mar 2026
Viewed by 701
Abstract
The interoperability of electronic health records in Colombia faces a critical gap between the regulatory mandates established by the Colombian regulatory framework and the actual technical capacity of healthcare institutions to implement them. This article presents PIRE (Electronic Records Interoperability Platform), an open-source [...] Read more.
The interoperability of electronic health records in Colombia faces a critical gap between the regulatory mandates established by the Colombian regulatory framework and the actual technical capacity of healthcare institutions to implement them. This article presents PIRE (Electronic Records Interoperability Platform), an open-source architecture that demonstrates the viability of end-to-end FHIR systems in the Colombian context. The main objective was to develop a platform capable of integrating health data from biomedical devices into an FHIR server, preserving clinical semantics through LOINC terminologies. The methodology followed an iterative development approach, implementing a HAPI FHIR server on AWS, a normalization application in Flask, and clinical visualization modules aligned with the FHIR Core CO Implementation Guide. The Bioharness-3 device was used to capture metrics on heart rate, respiratory rate, activity, and posture. The platform achieved a data normalization latency of 104–438 ms per record and 100% semantic validation against the FHIR Core CO profiles, validating compliance with Colombian IHCE specifications. It is concluded that PIRE constitutes a reproducible reference model for healthcare institutions that wish to implement interoperability as a cost-effective solution. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Medical Informatics)
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23 pages, 4967 KB  
Article
Comparative Evaluation of Machine Learning Models Using Structured and Unstructured Clinical Data for Predicting Unplanned General Medicine Readmissions in a Tertiary Hospital in Australia
by Yogesh Sharma, Campbell Thompson, Arduino A. Mangoni, Chris Horwood and Richard Woodman
Computers 2026, 15(3), 138; https://doi.org/10.3390/computers15030138 - 26 Feb 2026
Viewed by 602
Abstract
Background: Unplanned 30-day hospital readmissions, a key healthcare quality metric, are common and costly. Prediction models built on structured data often perform modestly, and the added value of unstructured clinical notes remains unclear. Methods: This retrospective cohort study included 4135 general medicine admissions [...] Read more.
Background: Unplanned 30-day hospital readmissions, a key healthcare quality metric, are common and costly. Prediction models built on structured data often perform modestly, and the added value of unstructured clinical notes remains unclear. Methods: This retrospective cohort study included 4135 general medicine admissions to a tertiary Australian hospital between July 2022 and June 2023. Structured predictors included demographics, comorbidities, frailty, prior healthcare utilisation, length-of-stay, inflammatory markers, socioeconomic indicators, and lifestyle factors. We developed deep learning models using structured data alone, unstructured text alone, and a combined multimodal architecture integrating both modalities. For benchmarking, multiple classical machine learning models trained on structured features were evaluated using identical data splits, including logistic regression, XGBoost, random forest, gradient boosting, extra trees, and HistGradient Boosting. Model performance was assessed on a hold-out test set using ROC-AUC, accuracy, precision, recall, and F1-score. Results: Unplanned readmissions occurred in 24.3% of admissions. Among classical machine learning models, logistic regression achieved the highest discrimination (ROC-AUC 0.64), with no substantial improvement observed from ensemble methods. Structured-only deep learning achieved ROC-AUC 0.62. Unstructured text-only and multimodal models achieved ROC-AUCs of 0.52 and 0.58, respectively. Although overall discrimination of the multimodal model was lower than structured-only performance, it demonstrated improved sensitivity and F1-score for identifying patients who were readmitted. Prior hospitalisations, emergency department visits, and comorbidity burden were the strongest predictors. Conclusions: Structured EMR variables remain the main drivers of 30-day readmission risk. More complex classical machine learning models did not outperform logistic regression, and incorporating unstructured clinical text provided only modest improvement in identifying high-risk patients without enhancing overall discrimination. Full article
(This article belongs to the Special Issue Artificial Intelligence (AI) in Medical Informatics)
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