Artificial Intelligence and Big Data Analytics for Sustainable Healthcare Systems
A special issue of Big Data and Cognitive Computing (ISSN 2504-2289). This special issue belongs to the section "Artificial Intelligence and Multi-Agent Systems".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 512
Special Issue Editors
Interests: big data; generative AI; association rules
Special Issues, Collections and Topics in MDPI journals
Interests: natural language processing; machine learning; generative AI; healthcare systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Sustainable healthcare increasingly depends on the effective use of large-scale medical, environmental, and operational data to support clinical, organisational, and public health decision-making. Advances in big data analytics, cognitive computing paradigms, and machine learning are creating new opportunities to design resilient, efficient, and equitable healthcare systems capable of adapting to demographic, environmental, and resource-based challenges.
This Special Issue, entitled “Artificial Intelligence and Big Data Analytics for Sustainable Healthcare Systems,” will highlight innovative research that integrates AI, health informatics, and data-driven computational methods to improve the sustainability of care delivery. We welcome research that leverages large, heterogeneous datasets, such as electronic health records, biomedical signals, imaging data, and environmental indicators, to enable predictive modelling, personalised medicine, and environmentally aware healthcare operations.
We encourage contributions addressing cognitive and intelligent computing approaches, interpretable and responsible AI, fairness-aware models, and the integration of data-driven systems into real clinical and public health workflows. Topics of interest include big data pipelines for healthcare, scalable machine learning for population health, intelligent decision support, AI-assisted epidemiological surveillance, the optimisation of medical resource allocation, and evaluations of the societal, economic, or ecological impacts of AI-enabled healthcare innovation.
This Special Issue will provide a multidisciplinary platform for advancing the development of sustainable, data-intensive, and human-centred healthcare systems.
We look forward to receiving your contributions.
You may choose our Joint Special Issue in Sustainability.
Dr. Carlos Fernández-Basso
Dr. Andrea Morales-Garzón
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.
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. Big Data and Cognitive Computing 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
- big data analytics in healthcare
- cognitive computing
- health informatics
- large language models (LLMs)
- autonomous AI Agents
- multi-agent systems
- machine learning and deep learning
- predictive modelling
- responsible and explainable AI
- electronic health records
- clinical decision support
- multimodal data integration
- sustainable healthcare systems
- environmental and population health data
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