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

Research synthesis · Healthcare informatics
Canonical page: https://www.narasimharao.net/research/focus-areas/healthcare-informatics/
Author: Narasimha Rao Vajjhala, Professor and Chair of Computer Science, American University in Bulgaria (ORCID 0000-0002-8260-2392)
Updated: 2026-09-25

> Vajjhala and colleagues argue that AI in healthcare succeeds only when it is transparent, accountable and rigorously validated, and they document how machine learning, IoT, 5G and efficiency analysis can support patient care and health-system management.

## 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)](https://www.narasimharao.net/research/rise-of-ai-in-healthcare-transparent-implementation/) 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](https://www.narasimharao.net/research/ai-in-healthcare-transparent-approach-informatics/)), and one applying explainable AI to clinical decision-making, telehealth, electronic health records, wearable monitoring and mental health ([Eappen et al., 2026b](https://www.narasimharao.net/research/enhancing-healthcare-informatics-transparent-explainable-ai/)).

The same standard of transparency shapes the group’s empirical work. [Haveri and Vajjhala (2026)](https://www.narasimharao.net/research/cnn-pneumonia-detection-chest-xray-efficientnetv2/) 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](https://www.narasimharao.net/research/smart-health-machine-learning-internet-of-things/)), and reviewed how 5G networks can enable smart healthcare applications after COVID-19 exposed overcrowded hospitals ([Vajjhala & Eappen, 2023](https://www.narasimharao.net/research/role-of-5g-networks-healthcare-applications/)). On health-system management, [Vajjhala and Eappen (2024a)](https://www.narasimharao.net/research/data-envelopment-analysis-healthcare-management-review/) 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](https://www.narasimharao.net/research/health-informatics-patient-safety-times-of-crisis/)). At the data-science end, [Biba and Vajjhala (2022)](https://www.narasimharao.net/research/statistical-relational-learning-genomics-review/) showed that statistical relational learning suits the relational, noisy and incomplete data of genomics, with the computational cost of inference as its main limitation.

## Key claims with sources

1. Liability concerns, lack of explainability and unclear accountability are the key barriers to healthcare AI adoption ([Eappen et al., 2026c](https://www.narasimharao.net/research/rise-of-ai-in-healthcare-transparent-implementation/)).
2. A lightweight EfficientNetV2B0 model detected paediatric pneumonia with AUC 0.967 and recall 0.98 ([Haveri & Vajjhala, 2026](https://www.narasimharao.net/research/cnn-pneumonia-detection-chest-xray-efficientnetv2/)).
3. Data envelopment analysis supports efficiency measurement and benchmarking in healthcare but is limited by input/output selection, outlier sensitivity and statistical noise ([Vajjhala & Eappen, 2024a](https://www.narasimharao.net/research/data-envelopment-analysis-healthcare-management-review/)).

## 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 and key findings: https://www.narasimharao.net/research/statistical-relational-learning-genomics-review/
- 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 and key findings: https://www.narasimharao.net/research/rise-of-ai-in-healthcare-transparent-implementation/
- 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 and key findings: https://www.narasimharao.net/research/ai-in-healthcare-transparent-approach-informatics/
- 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 and key findings: https://www.narasimharao.net/research/enhancing-healthcare-informatics-transparent-explainable-ai/
- 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 and key findings: https://www.narasimharao.net/research/cnn-pneumonia-detection-chest-xray-efficientnetv2/
- 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 and key findings: https://www.narasimharao.net/research/role-of-5g-networks-healthcare-applications/
- 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 and key findings: https://www.narasimharao.net/research/data-envelopment-analysis-healthcare-management-review/
- 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 and key findings: https://www.narasimharao.net/research/smart-health-machine-learning-internet-of-things/
- 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 and key findings: https://www.narasimharao.net/research/health-informatics-patient-safety-times-of-crisis/