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Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications

Submission Deadline: 31 January 2026 View: 1749 Submit to Special Issue

Guest Editors

Dr. Antonio Sarasa-Cabezuelo

Email: asarasa@ucm.es

Affiliation: Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, Madrid, 28040, Spain

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Research Interests: artificial intelligence, machine learning, medical informatics, public health, deep learning, generative artificial intelligence

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Dr. Ana M. Gonzlez de Miguel

Email: ana.gonzalez@fdi.ucm.es

Affiliation: Department of Software Engineering and Artificial Intelligence, Complutense University of Madrid, Madrid, 28040, Spain

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Research Interests: Generative artificial intelligence, Behavioral-based artificial intelligence, Software engineering methods

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Dr. Ulises Roman-Concha

Email: nromanc@unmsm.edu.pe

Affiliation: Faculty of System Engineering, Universidad Nacional Mayor de San Marcos UNMSM, Lima, 15081, Peru

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Research Interests: Data Mining, Big Data, Machine Learning, BI, Educacion Virtual

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Dr. Krishna Kumar Sharma

Email: krisshna.sharma@uok.ac.in

Affiliation: Department of Computer Science and Informatics, University of Kota, Kota, Rajasthan, 324005, India

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Research Interests: Data Mining, Big Data, Machine Learning

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Summary

The integration of artificial intelligence (AI) models in healthcare is transforming medical diagnostics, treatment planning, and patient management. However, their deployment in critical healthcare applications presents unique challenges, including issues of reliability, interpretability, and ethical concerns. While deep learning and other AI techniques have demonstrated remarkable success in medical imaging, predictive analytics, and personalized medicine, their black-box nature can hinder trust among healthcare professionals and patients. Addressing these challenges requires a comprehensive approach that balances model performance with transparency, robustness, and ethical considerations. This special issue aims to explore recent advancements, challenges, and applications of AI in healthcare, fostering discussions on how to enhance their reliability, effectiveness, and trustworthiness.

This special issue invites contributions that address both theoretical and practical advancements in the development, implementation, and evaluation of AI models in healthcare. The scope of this issue includes, but is not limited to:
· Theoretical foundations and methodological advancements in AI for healthcare
· Explainable and interpretable AI models in medical applications
· Deep learning applications in medical diagnostics and imaging
· AI-driven predictive analytics for disease progression and treatment outcomes
· Ethical, legal, and social implications of AI in healthcare
· Human-AI collaboration in clinical decision-making
· Bias, fairness, and generalizability of AI models in healthcare
· Evaluation metrics and benchmarking for AI in healthcare
· Regulatory frameworks and compliance challenges for AI-driven healthcare solutions

Keywords

Artificial Intelligence in Healthcare, Medical AI Models, Explainable AI in Medicine, Interpretable Machine Learning for Healthcare, AI-driven Diagnostics and Treatment Planning, Ethical and Regulatory Issues in Healthcare AI, Trustworthy AI for Clinical Decision Support, AI-based Predictive Analytics in Medicine

Published Papers

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Open Access

ARTICLE

Mordukhovich Subdifferential Optimization Framework for Multi-Criteria Voice Cloning of Pathological Speech
Rytis Maskeliūnas, Robertas Damaševičius, Audrius Kulikajevas, Kipras Pribuišis, Nora Ulozaitė-Stanienė, Virgilijus Uloza
CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.072790
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract This study introduces a novel voice cloning framework driven by Mordukhovich Subdifferential Optimization (MSO) to address the complex multi-objective challenges of pathological speech synthesis in under-resourced Lithuanian language with unique phonemes not present in most pre-trained models. Unlike existing voice synthesis models that often optimize for a single objective or are restricted to major languages, our approach explicitly balances four competing criteria: speech naturalness, speaker similarity, computational efficiency, and adaptability to pathological voice patterns. We evaluate four model configurations combining Lithuanian and English encoders, synthesizers, and vocoders. The hybrid model (English encoder, Lithuanian synthesizer, English More >

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Open Access

REVIEW

Deep Learning in Medical Image Analysis: A Comprehensive Review of Algorithms, Trends, Applications, and Challenges
Dawa Chyophel Lepcha, Bhawna Goyal, Ayush Dogra, Ahmed Alkhayyat, Prabhat Kumar Sahu, Aaliya Ali, Vinay Kukreja
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1487-1573, 2025, DOI:10.32604/cmes.2025.070964
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Medical image analysis has become a cornerstone of modern healthcare, driven by the exponential growth of data from imaging modalities such as MRI, CT, PET, ultrasound, and X-ray. Traditional machine learning methods have made early contributions; however, recent advancements in deep learning (DL) have revolutionized the field, offering state-of-the-art performance in image classification, segmentation, detection, fusion, registration, and enhancement. This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks, highlighting both foundational models and recent innovations. The article begins by introducing conventional techniques and their limitations, setting the… More >

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534

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108
Open Access

ARTICLE

DeepNeck: Bottleneck Assisted Customized Deep Convolutional Neural Networks for Diagnosing Gastrointestinal Tract Disease
Sidra Naseem, Rashid Jahangir, Nazik Alturki, Faheem Shehzad, Muhammad Sami Ullah
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2481-2501, 2025, DOI:10.32604/cmes.2025.072575
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Diagnosing gastrointestinal tract diseases is a critical task requiring accurate and efficient methodologies. While deep learning models have significantly advanced medical image analysis, challenges such as imbalanced datasets and redundant features persist. This study proposes a novel framework that customizes two deep learning models, NasNetMobile and ResNet50, by incorporating bottleneck architectures, named as NasNeck and ResNeck, to enhance feature extraction. The feature vectors are fused into a combined vector, which is further optimized using an improved Whale Optimization Algorithm to minimize redundancy and improve discriminative power. The optimized feature vector is then classified using artificial… More >

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303
Open Access

ARTICLE

Spectrotemporal Deep Learning for Heart Sound Classification under Clinical Noise Conditions
Akbare Yaqub, Muhammad Sadiq Orakzai, Muhammad Farrukh Qureshi, Zohaib Mushtaq, Imran Siddique, Taha Radwan
CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2503-2533, 2025, DOI:10.32604/cmes.2025.071571
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, necessitating efficient diagnostic tools. This study develops and validates a deep learning framework for phonocardiogram (PCG) classification, focusing on model generalizability and robustness. Initially, a ResNet-18 model was trained on the PhysioNet 2016 dataset, achieving high accuracy. To assess real-world viability, we conducted extensive external validation on the HLS-CMDS dataset. We performed four key experiments: (1) Fine-tuning the PhysioNet-trained model for binary (Normal/Abnormal) classification on HLS-CMDS, achieving 88% accuracy. (2) Fine-tuning the same model for multi-class classification (Normal, Murmur, Extra Sound, Rhythm Disorder), which yielded… More >

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114
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ARTICLE

Noninvasive Hemoglobin Estimation with Adaptive Lightweight Convolutional Neural Network Using Wearable PPG
Florentin Smarandache, Saleh I. Alzahrani, Sulaiman Al Amro, Ijaz Ahmad, Mubashir Ali
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3715-3735, 2025, DOI:10.32604/cmes.2025.068736
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Hemoglobin is a vital protein in red blood cells responsible for transporting oxygen throughout the body. Its accurate measurement is crucial for diagnosing and managing conditions such as anemia and diabetes, where abnormal hemoglobin levels can indicate significant health issues. Traditional methods for hemoglobin measurement are invasive, causing pain, risk of infection, and are less convenient for frequent monitoring. PPG is a transformative technology in wearable healthcare for noninvasive monitoring and widely explored for blood pressure, sleep, blood glucose, and stress analysis. In this work, we propose a hemoglobin estimation method using an adaptive lightweight… More >

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ARTICLE

A Hybrid CNN-Transformer Framework for Normal Blood Cell Classification: Towards Automated Hematological Analysis
Osama M. Alshehri, Ahmad Shaf, Muhammad Irfan, Mohammed M. Jalal, Malik A. Altayar, Mohammed H. Abu-Alghayth, Humood Al Shmrany, Tariq Ali, Toufique A. Soomro, Ali G. Alkhathami
CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1165-1196, 2025, DOI:10.32604/cmes.2025.067150
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract Background: Accurate classification of normal blood cells is a critical foundation for automated hematological analysis, including the detection of pathological conditions like leukemia. While convolutional neural networks (CNNs) excel in local feature extraction, their ability to capture global contextual relationships in complex cellular morphologies is limited. This study introduces a hybrid CNN-Transformer framework to enhance normal blood cell classification, laying the groundwork for future leukemia diagnostics. Methods: The proposed architecture integrates pre-trained CNNs (ResNet50, EfficientNetB3, InceptionV3, CustomCNN) with Vision Transformer (ViT) layers to combine local and global feature modeling. Four hybrid models were evaluated on… More >

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710
Open Access

ARTICLE

Enhanced Multimodal Physiological Signal Analysis for Pain Assessment Using Optimized Ensemble Deep Learning
Karim Gasmi, Olfa Hrizi, Najib Ben Aoun, Ibrahim Alrashdi, Ali Alqazzaz, Omer Hamid, Mohamed O. Altaieb, Alameen E. M. Abdalrahman, Lassaad Ben Ammar, Manel Mrabet, Omrane Necibi
CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2459-2489, 2025, DOI:10.32604/cmes.2025.065817
（This article belongs to the Special Issue: Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications)
Abstract The potential applications of multimodal physiological signals in healthcare, pain monitoring, and clinical decision support systems have garnered significant attention in biomedical research. Subjective self-reporting is the foundation of conventional pain assessment methods, which may be unreliable. Deep learning is a promising alternative to resolve this limitation through automated pain classification. This paper proposes an ensemble deep-learning framework for pain assessment. The framework makes use of features collected from electromyography (EMG), skin conductance level (SCL), and electrocardiography (ECG) signals. We integrate Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Bidirectional Gated Recurrent Units (BiGRU),… More >

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547

Artificial Intelligence Models in Healthcare: Challenges, Methods, and Applications

Guest Editors

Summary

Keywords

Published Papers

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96

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31

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534

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108

View

646

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303

View

263

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114

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1063

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381

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1406

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710

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1412

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547

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