Application of Artificial Intelligence in Disease Diagnosis and Treatment

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

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 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.

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