Further research is needed on

• How to optimize the presentation of AI recommendations to clinicians to prevent cognitive overload and ensure effective integration into clinical practice.
• How to enhance AI’s ability to provide meaningful explanations and interpretations that more intuitively reflect clinical reasoning patterns.
• How to best embed interpretable and explainable AI (XAI) decision support at optimal intervention points within existing clinical workflows, minimizing disruption while maximizing system utility and preserving clinical autonomy.

Further research is needed on:

• how to solve the fundamental tension between data privacy and model utility across the fragmented healthcare ecosystem. This involves
• Advancing Federated Learning (FL) to enable collaborative model training across multiple institutions without centralizing sensitive patient data.
• Addressing the key challenge of statistical heterogeneity (varied patient populations and data quality).
• Efficiently dispersing critical communications with limited bandwidth.

• How to build high-confidence AI systems that “know what they don’t know” for safe clinical deployment. This involves advancing uncertainty quantification to provide well-calibrated confidence estimates that distinguish between model limitations and inherent randomness.
• How to engineer robust defenses against distribution shifts and noisy data to develop confidence-aware explainable AI (XAI) that flags uncertainty rather than falling silent, which helps clinicians assess the trustworthiness of AI predictions.

• How to successfully deploy AI for remote health monitoring and bridge the gap between controlled in-patient settings (hospitals) and uncontrolled, variable settings (home environments).
• How to efficiently interpret data from diverse, non-standardized wearables using low-resource Edge AI and robust multimodal fusion.
• How to engineer context-aware models that distinguish health-related events from normal activities, create longitudinal behavioral models for personalized tracking, and ensure rigorous privacy in the home setting—while managing challenges like intermittent connectivity and refining patient-centered alert design.