Healthcare informatics: transparent, explainable and trustworthy AI for health systems

Synthesis

Narasimha Rao Vajjhala’s healthcare informatics research, much of it with Philip Eappen, centres on one claim: artificial intelligence improves health systems only when it is transparent, accountable and rigorously validated. Eappen et al. (2026c) identified professional liability concerns, the lack of explainability in algorithmic decision-making and unclear accountability for AI-influenced clinical outcomes as the key barriers to adoption, and argued that compliance with GDPR and HIPAA must be paired with ethical oversight and collaboration among patients, providers, developers, regulators and insurers. Two edited volumes develop this agenda: one on transparent AI across informatics, patient-centred care and clinical applications (Eappen et al., 2026a), and one applying explainable AI to clinical decision-making, telehealth, electronic health records, wearable monitoring and mental health (Eappen et al., 2026b).

The same standard of transparency shapes the group’s empirical work. Haveri and Vajjhala (2026) trained a lightweight EfficientNetV2B0 network on paediatric chest X-rays under CLAIM and TRIPOD-AI reporting standards and reported an AUC of 0.967 (95% CI 0.953–0.979) with pneumonia recall of 0.98, missing only about 2% of true cases. They reported openly that normal-class recall was lower (0.752) and calibration moderate, framing the false-positive tendency as a clinically preferable trade-off for triage.

Vajjhala and Eappen have also mapped how emerging technologies can support care delivery. They showed that combining machine learning with the Internet of Things supports early disease detection, personalised treatment and operational efficiency, provided that data privacy, model interpretability, bias mitigation and secure connectivity are addressed (Vajjhala & Eappen, 2024b), and reviewed how 5G networks can enable smart healthcare applications after COVID-19 exposed overcrowded hospitals (Vajjhala & Eappen, 2023). On health-system management, Vajjhala and Eappen (2024a) showed that data envelopment analysis is widely used for efficiency measurement, benchmarking and resource allocation in hospitals, nursing and outpatient services, while identifying six limitations including input/output selection, sensitivity to outliers and the inability to handle statistical noise.

The crisis dimension runs through the work: an edited volume documented how health informatics, telemedicine and clinical decision support performed during the COVID-19 pandemic and how they could protect patient safety in future crises (Vajjhala & Eappen, 2022). At the data-science end, Biba and Vajjhala (2022) showed that statistical relational learning suits the relational, noisy and incomplete data of genomics, with the computational cost of inference as its main limitation.

References

• Biba, M., & Vajjhala, N. R. (2022). Statistical Relational Learning for Genomics Applications: A State-of-the-Art Review. In S. S. Roy, Y.-H. Taguchi (Eds.), Handbook of Machine Learning Applications for Genomics (pp. 31–42). Springer Nature Singapore Pte Ltd.. https://doi.org/10.1007/978-981-16-9158-4_3 Summary & key findings →
• Eappen, P., Gunn, V., Zikos, D., & Vajjhala, N. R. (2026c). The Rise of AI in Healthcare: A Transparent Approach to Implementation. In Philip Eappen, Virginia Gunn, Dimitrios Zikos, Narasimha Rao Vajjhala (Eds.), AI in Healthcare: A Transparent Approach to Informatics (pp. 3–16). Chapman and Hall/CRC. https://doi.org/10.1201/9781003624875-2 Summary & key findings →
• Eappen, P., Gunn, V., Zikos, D., & Vajjhala, N. R. (Eds.). (2026a). AI in Healthcare: A Transparent Approach to Informatics. Chapman and Hall/CRC. https://doi.org/10.1201/9781003624875 Summary & key findings →
• Eappen, P., Vajjhala, N. R., Guo, R., Shinners, L., & Gunn, V. (Eds.). (2026b). Enhancing Healthcare Informatics with Transparent and Explainable AI. Auerbach Publications (CRC Press). https://doi.org/10.1201/9781003604440 Summary & key findings →
• Haveri, K., & Vajjhala, N. R. (2026). Deep Learning for Medical Image Analysis: CNN-based Pneumonia Detection on Chest X-Rays. In 2026 6th International Conference on Pervasive Computing and Social Networking (ICPCSN) (pp. 156–161). IEEE. https://doi.org/10.1109/ICPCSN68523.2026.11543623 Summary & key findings →
• Vajjhala, N. R., & Eappen, P. (2023). The Role of 5G Networks in Healthcare Applications. In Ambar Bajpai, Arun Balodi (Eds.), Applications of 5G and Beyond in Smart Cities (pp. 87–98). CRC Press. https://doi.org/10.1201/9781003227861-5 Summary & key findings →
• Vajjhala, N. R., & Eappen, P. (2024a). Data Envelopment Analysis in Healthcare Management: Overview of the Latest Trends. In Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency (pp. 245–260). IGI Global. https://doi.org/10.4018/979-8-3693-0255-2.ch011 Summary & key findings →
• Vajjhala, N. R., & Eappen, P. (2024b). Smart Health: Advancements in Machine Learning and the Internet of Things Solutions. In Pijush Samui, Sanjiban Sekhar Roy, Wengang Zhang, Y-h. Taguchi (Eds.), Machine Learning and IoT Applications for Health Informatics (pp. 31–51). CRC Press. https://doi.org/10.1201/9781003424987-3 Summary & key findings →
• Vajjhala, N. R., & Eappen, P. (Eds.). (2022). Health Informatics and Patient Safety in Times of Crisis. IGI Global. https://doi.org/10.4018/978-1-6684-5499-2 Summary & key findings →

How to cite this synthesis

Please cite the original publications above for specific findings. To cite this overview itself:

Vajjhala, N. R. (2026, September 25). Healthcare informatics: transparent, explainable and trustworthy AI for health systems. Narasimha Rao Vajjhala. https://www.narasimharao.net/research/focus-areas/healthcare-informatics/