The Virtual Doctor: An Interactive Artificial Intelligence based on Deep Learning for Non-Invasive Prediction of Diabetes

TL;DR

This paper presents a novel AI system acting as a virtual doctor that interacts with patients using speech, predicts type 2 diabetes non-invasively with deep learning, and assesses user acceptance for future healthcare integration.

Contribution

It introduces an interactive AI with speech capabilities for medical diagnosis and demonstrates its effectiveness in predicting diabetes non-invasively, a novel approach in telemedicine.

Findings

AI can accurately predict T2DM using non-invasive sensors

Patients show positive acceptance towards AI-based healthcare systems

The system provides interpretable probability estimates for diagnosis

Abstract

Artificial intelligence (AI) will pave the way to a new era in medicine. However, currently available AI systems do not interact with a patient, e.g., for anamnesis, and thus are only used by the physicians for predictions in diagnosis or prognosis. However, these systems are widely used, e.g., in diabetes or cancer prediction. In the current study, we developed an AI that is able to interact with a patient (virtual doctor) by using a speech recognition and speech synthesis system and thus can autonomously interact with the patient, which is particularly important for, e.g., rural areas, where the availability of primary medical care is strongly limited by low population densities. As a proof-of-concept, the system is able to predict type 2 diabetes mellitus (T2DM) based on non-invasive sensors and deep neural networks. Moreover, the system provides an easy-to-interpret probability estimation for T2DM for a given patient. Besides the development of the AI, we further analyzed the acceptance of young people for AI in healthcare to estimate the impact of such system in the future.