Emerging Artificial Intelligence Trends for Predictive Analytics and Personalized Healthcare

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1453

Special Issue Editors


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Electronic Technology Department, Faculty of Engineering of Gipuzkoa, University of the Basque Country, 20018 San Sebastian, Spain
Interests: artificial intelligence; physical activity analysis; medical image analysis

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Guest Editor
1. Department of Physiology, University of the Basque Country, 48940 Leioa, Spain
2. Biobizkaia Health Research Institute, 48903 Barakaldo, Spain
Interests: physiology; aging; frailty; molecular biomarkers; sarcopenia; physical function; physical activity; bioethics
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Computational Intelligence Group, Department of CCIA, University of the Basque Country, 20018 San Sebastian, Spain
Interests: hyperspectral image analysis; computational intelligence; medical imaging
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Special Issue Information

Dear Colleagues,

The next big technology to revolutionize healthcare will be artificial intelligence (AI). AI is increasingly present in the prevention, diagnosis, treatment and monitoring phases of subjects. The models that are being implemented allow for the implementation of personalized medicine, which aims to offer tailored strategies for defined groups of people. The use of AI includes processing data acquired through wearable sensors or medical devices; analyzing medical images; clinical decision making; algorithms designed to help monitor patients; and population health management.

With regard to AI models, in addition to the most commonly used classical methods—such as Artificial Neural Networks and Genetic Algorithms—Deep Neural Networks, emerging trends like Generative Adversarial Networks (GANs), and Federated Learning are technologies that have the capacity to improve the outcomes of these processes.  

In this context, predictive analytics has emerged as a transformative tool to enhance the healthcare sector, offering the ability to process large amounts of patient data for the prediction and prevention of diseases, the optimization of treatment plans, and the enhancement of healthcare services.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Machine learning in medicine, medically oriented human biology, and healthcare.
  • Advanced data processing for human physiology.
  • Wearable sensor data processing for the assessment of health conditions.
  • Data analytics and mining for biomedical decision support.
  • AI models leading to personalized medicine for the prediction and prevention, diagnosis, and treatment of disease.

Dr. Josu Maiora
Dr. Maria Begoña Sanz Echevarría
Prof. Dr. Manuel Graña
Guest Editors

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Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • predictive analytics
  • wearable sensors
  • medical devices
  • medical image analysis

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Published Papers (1 paper)

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Review

21 pages, 1850 KB  
Review
Explainable Agentic Artificial Intelligence in Healthcare: A Scoping Review
by Bernardo G. Collaco, Srinivasagam Prabha, Cesar A. Gomez-Cabello, Syed Ali Haider, Ariana Genovese, Nadia G. Wood, Narayanan Gopala, Raghunath Raman, Erik O. Hester and Antonio Jorge Forte
Bioengineering 2026, 13(5), 513; https://doi.org/10.3390/bioengineering13050513 (registering DOI) - 28 Apr 2026
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Abstract
Background: Agentic artificial intelligence (AI) systems, characterized by autonomous goal-directed behavior, multi-step reasoning, task decomposition, and tool use, are increasingly proposed for healthcare applications. However, their autonomy raises concerns regarding transparency, accountability, and human oversight. While explainable AI (XAI) has been widely studied [...] Read more.
Background: Agentic artificial intelligence (AI) systems, characterized by autonomous goal-directed behavior, multi-step reasoning, task decomposition, and tool use, are increasingly proposed for healthcare applications. However, their autonomy raises concerns regarding transparency, accountability, and human oversight. While explainable AI (XAI) has been widely studied in traditional predictive models, less is known about how explainability is implemented within agentic architectures. Objective: To map the emerging literature on explainable agentic AI (XAAI) in healthcare and characterize the types, scope, and forms of explainability used in these systems. Methods: A scoping review was conducted following PRISMA-ScR guidelines. PubMed, Embase, IEEE Xplore, and ACM Digital Library were searched through November 2025. Eligible studies described healthcare-related agentic AI systems incorporating explicit explainability mechanisms. Data were extracted on system architecture, explainability type (intrinsic, post hoc, hybrid), explanation scope (local, global), explanation form, and reported clinical outcomes. Results: Nine studies met the inclusion criteria. All systems demonstrated core agentic features, including autonomy, task decomposition, and tool integration, often within multi-agent frameworks. Explainability was predominantly intrinsic and workflow-native, typically delivered through textual reasoning traces and example-based grounding in retrieved clinical evidence. Feature-based and global explanations were comparatively rare and largely confined to hybrid architectures. Across domains including radiology, neurology, psychiatry, and biomedical research, XAAI systems were reported to improve performance and interpretability relative to baseline models in the included studies. However, these findings were derived from heterogeneous, predominantly experimental or retrospective studies, and structured human-in-the-loop oversight was infrequently described. Conclusions: Current XAAI systems appear to emphasize process transparency and evidence grounding rather than mechanistic model-level attribution. The available evidence remains limited and heterogeneous, and findings should be interpreted as early trends rather than established characteristics. Further progress will require standardized evaluation frameworks, clearer reporting of oversight mechanisms, and validation in real-world clinical settings to support safe and trustworthy integration of agentic AI into healthcare practice. Full article
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