Abstract

Challenging clinicians and patients, AI chatbots are changing the nature of healthcare by

offering interactive decision support. Their adoption however, hinges significantly on ex-

plainability and ethical integrity so as to lead to trust, accountability and patient safety.

This paper is a systematic framework on how explainable and ethical AI chatbots could

be developed to support health system decision support systems. The model incorporates

superior explainability methods like LIME and SHAP with high ethical standards including

fairness, transparency, privacy, and informed consent. Overall development, and assessment

is through inclusive stakeholder engagement. The prototype chatbot has secure data process-

ing and clear user interfaces. Strict assessment has shown that the accuracy, usability, trust,

and ethical standards have improved, and complex data interpretation and bias reduction have

been identified as a problem. The future competitive needs include adaptive AI, better gov-

ernance, and expansion of clinical integration. The piece contributes towards the responsible

adoption of AI in healthcare, which creates safer and more equitable digital health solutions.

Reference

Statistics