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| References | Method | Contributions | Domain |
|
| Bao et al. [33] | Hybrid model chatbot that combines knowledge graph and a text similarity model | Proposed method was able to identify and reduce similarity in large QA dataset | Generic |
| Harilal et al. [36] | Developed CARO, a chatbot app, which is capable of performing empathetic conversations and providing medical advice | Proposed method has the ability to sense the conversational context, intent, and associated emotions | Depression |
| Bibault et al. [28] | Vik chatbot: blind, randomized controlled noninferiority trial | Proposed method was able to improve conversation between chatbot and physician | Breast cancer |
| Bali et al. [50] | Ensemble learning using advanced NLU techniques | Improved accuracy in diabetes prediction when compared to generic health prediction | Diabetes and generic |
| Cameron et al. [51] | Proposed iHelper using questionnaire developed by chatbottest | Recommendations to increase the usability of a chatbot for mental healthcare | Mental healthcare |
| Chaix et al. [34] | Vik chatbot | Evaluated a yearlong of conversations between patients with breast cancer and a chatbot | Breast cancer |
| Chung and Park [30] | Chatbot-based healthcare service framework in cloud | Provides a smooth human-machine interaction for the chatbot healthcare service | Accident response |
| Hussein and Athula [38] | Virtual Diabetes Management System (VDMS) using modified open source AIML web-based chatbot | Proposed method provides a more robust knowledge using Wikipedia knowledge | Generic and diabetes patients |
| Huang et al. [37] | AI-based health chatbot known as “Smart Wireless Interactive Healthcare System” (SWITCHes) | Proposed system can provide advice to user on food intake based on calorie order, advice on physical activities, etc. | Weight control and health promotion |
| Ahmad et al. [31] | NLP | Ability to give adequate advice on the right type of medication based on information provided | Pharmacy |
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