TY - GEN
T1 - AI Medical School Tutor
T2 - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020
AU - Afzal, Shazia
AU - Dhamecha, Tejas Indulal
AU - Gagnon, Paul
AU - Nayak, Akash
AU - Shah, Ayush
AU - Carlstedt-Duke, Jan
AU - Pathak, Smriti
AU - Mondal, Sneha
AU - Gugnani, Akshay
AU - Zary, Nabil
AU - Chetlur, Malolan
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - In this paper we present our experience in the design, modelling, implementation and evaluation of a conversational medical school tutor (MST), employing AI on the cloud. MST combines case-based tutoring with competency based curriculum review, using a natural language interface to enable an adaptive and rich learning experience. It is designed both to engage and tutor medical students through Digital Virtual Patient (DVP) interactions built around clinical reasoning activities and their application of foundational knowledge. DVPs in MST are realistic clinical cases authored by subject matter experts in natural language text. The context of each clinical case is modelled as a set of complex concepts with their associated attributes and synonyms using the UMLS ontology. The MST conversational engine understands the intent of the user’s natural language inputs by training Watson Assistant service and drives a meaningful dialogue relevant to the clinical case under investigation. The curriculum content is analysed using NLP techniques and represented as a related and cohesive graph with concepts as its nodes. The runtime application is modelled as a dynamic and adaptive flow between the case and student characteristics. We describe in detail the various challenges encountered in the design and implementation of this intelligent tutor and also present evaluation of the tutor through two field trials with third and fourth year students comprising of 90 medical students.
AB - In this paper we present our experience in the design, modelling, implementation and evaluation of a conversational medical school tutor (MST), employing AI on the cloud. MST combines case-based tutoring with competency based curriculum review, using a natural language interface to enable an adaptive and rich learning experience. It is designed both to engage and tutor medical students through Digital Virtual Patient (DVP) interactions built around clinical reasoning activities and their application of foundational knowledge. DVPs in MST are realistic clinical cases authored by subject matter experts in natural language text. The context of each clinical case is modelled as a set of complex concepts with their associated attributes and synonyms using the UMLS ontology. The MST conversational engine understands the intent of the user’s natural language inputs by training Watson Assistant service and drives a meaningful dialogue relevant to the clinical case under investigation. The curriculum content is analysed using NLP techniques and represented as a related and cohesive graph with concepts as its nodes. The runtime application is modelled as a dynamic and adaptive flow between the case and student characteristics. We describe in detail the various challenges encountered in the design and implementation of this intelligent tutor and also present evaluation of the tutor through two field trials with third and fourth year students comprising of 90 medical students.
KW - Case-based tutoring
KW - Digital virtual patient
KW - Natural language interface
UR - http://www.scopus.com/inward/record.url?scp=85092248316&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59137-3_13
DO - 10.1007/978-3-030-59137-3_13
M3 - Conference contribution
AN - SCOPUS:85092248316
SN - 9783030591366
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 133
EP - 145
BT - Artificial Intelligence in Medicine - 18th International Conference on Artificial Intelligence in Medicine, AIME 2020, Proceedings
A2 - Michalowski, Martin
A2 - Moskovitch, Robert
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 25 August 2020 through 28 August 2020
ER -