Artificial intelligence is increasingly embedded in clinical decision support, imaging, clinical natural language processing, patient-facing chatbots, remote monitoring, precision medicine, public-health surveillance and health-information management. Generative AI and foundation models have expanded these applications to multimodal reasoning, documentation support, triage, record summarization and knowledge discovery. Their reliability, however, depends on complex digital data ecosystems that include electronic health records, imaging repositories, claims, wearable and sensor data, patient-generated data and public-health datasets.
This special issue examines health AI as a data-intensive socio-technical system. It invites research on the data technologies, information-management practices, governance arrangements, human-AI interaction and empirical evaluation methods that determine whether AI can be trusted and adopted in real healthcare settings. Trustworthiness encompasses human oversight, technical robustness and safety, privacy and data governance, transparency, fairness, accountability and lifecycle monitoring.
We welcome interdisciplinary computational, information-systems, information-science, health-informatics, social-science, design-science and policy-oriented contributions with relevant empirical content, rigorous evaluation or systematic review. Full research articles, systematic reviews and short communications may use machine-learning experiments, benchmark evaluation, human-subject studies, surveys, interviews, case studies, longitudinal analysis, digital-trace analysis, bibliometric methods or mixed-methods research. Submissions should move beyond proof-of-concept development toward trustworthy deployment in clinical, public-health and patient-facing settings.
List of Topic Areas
- Data quality, provenance, bias, missingness, and representativeness in AI-enabled healthcare.
Interoperability, linked data, semantic web, knowledge graphs, and standards for digital health AI. - Explainable, transparent, and auditable AI systems for clinical and public health decision-making.
Human-centered AI, clinician-AI collaboration, workflow integration, and user trust in health settings. - Patient-facing AI, health chatbots, generative AI assistants, and online health information behavior.
- Clinical natural language processing, large language models, multimodal models, and evaluation in health contexts.
- Privacy-preserving analytics, federated learning, secure data sharing, and health data governance.
- Fairness, equity, and inclusion in AI models trained on health and social data.
- Real-world evidence, post-deployment monitoring, model drift, and lifecycle governance for health AI.
- Recommendation, classification, prediction, and decision support using electronic health record, imaging, wearable, claims, or public health data.
Submission Information
Submissions of full manuscripts are made using ScholarOne Manuscripts. Registration and access are available here:
Author guidelines must be strictly followed. Please see:
Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to “Please select the issue you are submitting to”.
Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.
Journal Information: Scopus Journal Q2, H-Index 40
Key Dates
Opening date for manuscript submissions: 1 October 2026
Closing date for manuscript submissions: 30 June 2027
For more details refer here
