Automated Medical Chatbot

Mishra, Abhishek; Mandal, Krishnasis

doi:10.2139/ssrn.3090881

Cited by 30 publications

(7 citation statements)

References 7 publications

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“…A text-to-text chatbot by Divya et al [ 32 ] engages patients regarding their medical symptoms to provide a personalized diagnosis and connects the user with the appropriate physician if major diseases are detected. Rarhi et al [ 33 ] proposed a similar design that provides a diagnosis based on symptoms, measures the seriousness, and connects users with a physician if needed [ 33 ]. In general, these systems may greatly help individuals in conducting daily check-ups, increase awareness of their health status, and encourage users to seek medical assistance for early intervention.…”

Section: Resultsmentioning

confidence: 99%

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Xu¹,

Sanders²,

Li³

et al. 2021

JMIR Cancer

234

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Background Chatbot is a timely topic applied in various fields, including medicine and health care, for human-like knowledge transfer and communication. Machine learning, a subset of artificial intelligence, has been proven particularly applicable in health care, with the ability for complex dialog management and conversational flexibility. Objective This review article aims to report on the recent advances and current trends in chatbot technology in medicine. A brief historical overview, along with the developmental progress and design characteristics, is first introduced. The focus will be on cancer therapy, with in-depth discussions and examples of diagnosis, treatment, monitoring, patient support, workflow efficiency, and health promotion. In addition, this paper will explore the limitations and areas of concern, highlighting ethical, moral, security, technical, and regulatory standards and evaluation issues to explain the hesitancy in implementation. Methods A search of the literature published in the past 20 years was conducted using the IEEE Xplore, PubMed, Web of Science, Scopus, and OVID databases. The screening of chatbots was guided by the open-access Botlist directory for health care components and further divided according to the following criteria: diagnosis, treatment, monitoring, support, workflow, and health promotion. Results Even after addressing these issues and establishing the safety or efficacy of chatbots, human elements in health care will not be replaceable. Therefore, chatbots have the potential to be integrated into clinical practice by working alongside health practitioners to reduce costs, refine workflow efficiencies, and improve patient outcomes. Other applications in pandemic support, global health, and education are yet to be fully explored. Conclusions Further research and interdisciplinary collaboration could advance this technology to dramatically improve the quality of care for patients, rebalance the workload for clinicians, and revolutionize the practice of medicine.

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Section: Resultsmentioning

confidence: 99%

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Xu¹,

Sanders²,

Li³

et al. 2021

JMIR Cancer

234

View full text Add to dashboard Cite

show abstract

“…Many text understanding approaches can fall under pattern matching. The different pattern matching methods reported in the included studies are as follows: keyword matching or string matching, where specific keywords or strings of the user input are matched to scripts stored in the database, which was used in 11 studies [ 10 - 15 , 32 , 33 , 46 , 48 , 50 ], or Extensible Markup Language (XML) variants, such as Artificial Intelligence Markup Language (AIML), which were used in 7 studies [ 16 , 17 , 34 - 37 , 47 ]. The second most popular approach was the use of machine learning methods for text understanding, which was used in 6 studies.…”

Section: Resultsmentioning

confidence: 99%

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

et al. 2020

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Background Chatbots are applications that can conduct natural language conversations with users. In the medical field, chatbots have been developed and used to serve different purposes. They provide patients with timely information that can be critical in some scenarios, such as access to mental health resources. Since the development of the first chatbot, ELIZA, in the late 1960s, much effort has followed to produce chatbots for various health purposes developed in different ways. Objective This study aimed to explore the technical aspects and development methodologies associated with chatbots used in the medical field to explain the best methods of development and support chatbot development researchers on their future work. Methods We searched for relevant articles in 8 literature databases (IEEE, ACM, Springer, ScienceDirect, Embase, MEDLINE, PsycINFO, and Google Scholar). We also performed forward and backward reference checking of the selected articles. Study selection was performed by one reviewer, and 50% of the selected studies were randomly checked by a second reviewer. A narrative approach was used for result synthesis. Chatbots were classified based on the different technical aspects of their development. The main chatbot components were identified in addition to the different techniques for implementing each module. Results The original search returned 2481 publications, of which we identified 45 studies that matched our inclusion and exclusion criteria. The most common language of communication between users and chatbots was English (n=23). We identified 4 main modules: text understanding module, dialog management module, database layer, and text generation module. The most common technique for developing text understanding and dialogue management is the pattern matching method (n=18 and n=25, respectively). The most common text generation is fixed output (n=36). Very few studies relied on generating original output. Most studies kept a medical knowledge base to be used by the chatbot for different purposes throughout the conversations. A few studies kept conversation scripts and collected user data and previous conversations. Conclusions Many chatbots have been developed for medical use, at an increasing rate. There is a recent, apparent shift in adopting machine learning–based approaches for developing chatbot systems. Further research can be conducted to link clinical outcomes to different chatbot development techniques and technical characteristics.

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“…[12] analyzed the landscape of health ChatBots by focusing on three basic questions, what kind of diseases are the ChatBots encountering, what patient's skills do ChatBots aim for and who are the most interested ChatBot technology providers in the health sector. Automated medical bot is the subject of another interesting paper from Krishnendu Rarhi, et al [13]. Battineni et al designed and deployed a chatbot that can help patients living in remote areas by promoting preventive measures, virus updates, and reducing psychological damage caused by isolation and fear during the Covid pandemic [33].…”

Section: Conversational Ai Tool One Of the Most Powerful Platforms Fo...mentioning

confidence: 99%

The Health ChatBots in Telemedicine: Intelligent Dialog System for Remote Support

Vasileiou

2022

Journal of Healthcare Engineering

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The objective of the Health Bot is to analyze and elaborate on the implementation of an intelligent system that can support telemedicine services using natural language processing (NLP) technology. Health Bot is a robust, modularized, and user-friendly platform that aims to improve the patient’s interactions with the health care system. Using NLP and speech recognition algorithms, the platform can analyze and classify free text and voice input data to symptoms. The classified data are used in the machine learning (ML) training process of the artificial intelligence (AI) models in order to predict the probability of the patient suffering from a specific disease and get alerted in case of disorders. The Health Bot is acting as a medical virtual agent in order to collect any information needed in the sense of a medical interview and provide medical assessments, close appointments with the doctors, and monitor/record the patient’s health condition. In this paper, two models have been trained for the Covid-19 and heart disease cases with 98.3% and 82% accuracy, respectively.

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Automated Medical Chatbot

Cited by 30 publications

References 7 publications

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review

Technical Aspects of Developing Chatbots for Medical Applications: Scoping Review

The Health ChatBots in Telemedicine: Intelligent Dialog System for Remote Support

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