TY - JOUR
T1 - The virtual doctor
T2 - An interactive clinical-decision-support system based on deep learning for non-invasive prediction of diabetes
AU - Spänig, Sebastian
AU - Emberger-Klein, Agnes
AU - Sowa, Jan Peter
AU - Canbay, Ali
AU - Menrad, Klaus
AU - Heider, Dominik
N1 - Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/9
Y1 - 2019/9
N2 - 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 a system in the future.
AB - 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 a system in the future.
KW - Artificial intelligence
KW - Deep learning
KW - Diabetes
KW - Diagnostics
KW - E-health
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85071558620&partnerID=8YFLogxK
U2 - 10.1016/j.artmed.2019.101706
DO - 10.1016/j.artmed.2019.101706
M3 - Article
C2 - 31607340
AN - SCOPUS:85071558620
SN - 0933-3657
VL - 100
JO - Artificial Intelligence in Medicine
JF - Artificial Intelligence in Medicine
M1 - 101706
ER -