OlloBot - Towards A Text-Based Arabic Health Conversational Agent: Evaluation and Results
By Ahmed Fadhil, Ahmed Ghassan Tawfiq AbuRa'ed. 2019
36 citations recorded by the source corpus.
We introduce OlloBot, an Arabic conversational agent that assists physicians and supports patients with the care process. It doesn’t replace the physicians, instead provides health tracking and support and assists physicians with the care delivery through a conversation medium. The current model comprises healthy diet, physical activity, mental health, in addition to food logging. Not only OlloBot tracks user daily food, it also offers useful tips for healthier living. We will discuss the design, development and testing of OlloBot, and highlight the findings and limitations arose from the testing.
Subfields: Applied Psychology, Artificial Intelligence, General Health Professions, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Y-Lan Boureau
- Emily Dinan
- Gavin Abercrombie
- Shannon L. Spruit
- Dirk Hovy
- A. S. Bergman
- Verena Rieser
- Jason Aaron Edward Weston
In General Health Professions
In Natural Language Processing
- Y-Lan Boureau
- Emily Dinan
- Gavin Abercrombie
- Dirk Hovy
- Shannon L. Spruit
- Verena Rieser
- A. S. Bergman
- Jason Aaron Edward Weston
Citations in the corpus
- Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design (2022, background): “Conversational systems designed for discussing health issues tend to not be generative models and use expert-produced rather than generic data (e.g. Brixey et al., 2017; Fadhil and AbuRa’ed, 2019; Vaira et al., 2018; Pereira and Díaz, 2019).”