Observational analysis demonstrates improvements in patient engagement using conversational AI in telemedicine, highlighting enhanced symptom triaging.
The use of Conversational Artificial intelligence (AI) in telemedicine has become an ultimate solution towards increasing patient interactions and improving the effectiveness of symptom triaging exercises. As more people request affordable and scaled-down healthcare services, AI-empowered chatbots and virtual automatons present up-to-date, interactive, and contextual medical assistance. The present paper discusses how it is possible to apply Conversational AI technologies in real-time interactions with patients, automatic symptoms evaluation, and more effective dialogue between patients and healthcare professionals. The paper looks into the architecture, functionalities and performance of telemedicinebased AI conversational systems. By comparing knowledge and going through case studies, we illustrate the usefulness of Conversational AI in clinician burden decrease and increasing patient satisfaction as well as enabling effective pre-diagnostic studies. The results reveal the issues of data privacy, data interpretability, and clinical validation, which provide a guide to ethical and successful implementation of AI in health models in the future.
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Veerendra Nath Jasthi (2024) studied this question.
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