Artificial Intelligence based Chat Bot for Patient Health Care

Anandan, P.; Kokila, S; Elango, Shrilekha; Gopinath, P; Sudarsan, P

doi:10.1109/iccci54379.2022.9740912

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…These language-based models have the ability to interact with users via text messages [20]. Artificial intelligencebased chatbots have paved their way into various fields, such as investment, education, and customer service, and are now being in research for use in the field of healthcare as well [21][22][23].…”

Section: Discussionmentioning

confidence: 99%

The Potential Usefulness of ChatGPT in Oral and Maxillofacial Radiology

Mago¹,

Sharma²

2023

Cureus

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Aim This study aimed to evaluate the potential usefulness of Chat Generated Pre-Trained Transformer-3 (ChatGPT-3) in oral and maxillofacial radiology for report writing by identifying radiographic anatomical landmarks and learning about oral and maxillofacial pathologies and their radiographic features. The study also aimed to evaluate the performance of ChatGPT-3 and its usage in oral and maxillofacial radiology training. Materials and methods A questionnaire consisting of 80 questions was queried on the OpenAI app ChatGPT-3. The questions were stratified based on three categories. The categorization was based on random anatomical landmarks, oral and maxillofacial pathologies, and the radiographic features of some of these pathologies. One oral and maxillofacial radiologist evaluated queries that were answered by the ChatGPT-3 model and rated them on a 4-point, modified Likert scale. The post-survey analysis for the performance of ChatGPT-3 was based on the Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, its application in oral and maxillofacial radiology training, and its recommended use. Results In order of efficiency, Chat GPT-3 gave 100% accuracy in describing radiographic landmarks. However, the content of the oral and maxillofacial pathologies was limited to major or characteristic radiographic features. The mean scores for the queries related to the anatomic landmarks, oral and maxillofacial pathologies, and radiographic features of the oral and maxillofacial pathologies were 3.94, 3.85, and 3.96, respectively. However, the median and mode scores were 4 and were similar to all categories. The data for the oral and maxillofacial pathologies when the questions were not specifically included in the format of the introduction of the pathology, causes, symptoms, and treatment. Out of two abbreviations, one was not answered correctly. Conclusion The study showed that ChatGPT-3 is efficient in describing the pathology, characteristic radiographic features, and describing anatomical landmarks. ChatGPT-3 can be used as an adjunct when an oral radiologist needs additional information on any pathology, however, it cannot be the mainstay for reference. ChatGPT-3 is less detail-oriented, and the data has a risk of infodemics and the possibility of medical errors. However, Chat GPT-3 can be an excellent tool in helping the community in increasing the knowledge and awareness of various pathologies and decreasing the anxiety of the patients while dental healthcare professionals formulate an appropriate treatment plan.

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Section: Discussionmentioning

confidence: 99%

The Potential Usefulness of ChatGPT in Oral and Maxillofacial Radiology

Mago¹,

Sharma²

2023

Cureus

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“…By providing immediate access to medical information and facilitating appointments with specialists, such chatbots offer potential avenues for enhancing patient care and reducing mortality rates. The integration of Natural Language Processing (NLP) and Machine Learning techniques further enhances the functionality and usability of these systems, highlighting their potential to revolutionize healthcare delivery in India [11]. This review of the literature highlights the long history of development and study in the field of line-following robots.…”

Section: Literature Reviewmentioning

confidence: 99%

Robotic Guardians: Navigating Hospital Isolation Zones for Disease Control

Keskar

2024

IJRASET

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In healthcare contexts where human access is limited, particularly in isolation and quarantine rooms, autonomous solutions become critical. This study presents a novel solution to addressing this problem: a line-following robot built for use in hospital isolation units. The robot's operating principle is led by a color recognition system, which responds dynamically to certain colors linked with directing cues. The system's ability to capture vital information by utilizing the ESP32 camera module increases its utility beyond navigation, extending to the collection of patient vitals displayed beside beds. By combining precise navigation with information gathering capabilities, this line-following robot offers a potential solution for tasks in restricted access healthcare environments.

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“…For instance, in India, an AI-powered chatbot assists patients by responding to their queries and facilitating appointment scheduling. This chatbot utilizes decision tree classifier and dimensionality reduction techniques to enhance its effectiveness [10]. These conversational agents serve various roles in healthcare, from collecting essential patient information to offering preliminary diagnoses.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care

Borna,

Gomez-Cabello,

Pressman

et al. 2024

EJIHPE

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In postoperative care, patient education and follow-up are pivotal for enhancing the quality of care and satisfaction. Artificial intelligence virtual assistants (AIVA) and large language models (LLMs) like Google BARD and ChatGPT-4 offer avenues for addressing patient queries using natural language processing (NLP) techniques. However, the accuracy and appropriateness of the information vary across these platforms, necessitating a comparative study to evaluate their efficacy in this domain. We conducted a study comparing AIVA (using IBM Watson Assistant) with ChatGPT-4 and Google BARD, assessing the accuracy, knowledge gap, and response appropriateness. AIVA demonstrated superior performance, with significantly higher accuracy (mean: 0.9) and lower knowledge gap (mean: 0.1) compared to BARD and ChatGPT-4. Additionally, AIVA’s responses received higher Likert scores for appropriateness. Our findings suggest that specialized AI tools like AIVA are more effective in delivering precise and contextually relevant information for postoperative care compared to general-purpose LLMs. While ChatGPT-4 shows promise, its performance varies, particularly in verbal interactions. This underscores the importance of tailored AI solutions in healthcare, where accuracy and clarity are paramount. Our study highlights the necessity for further research and the development of customized AI solutions to address specific medical contexts and improve patient outcomes.

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Artificial Intelligence based Chat Bot for Patient Health Care

Cited by 12 publications

References 6 publications

The Potential Usefulness of ChatGPT in Oral and Maxillofacial Radiology

The Potential Usefulness of ChatGPT in Oral and Maxillofacial Radiology

Robotic Guardians: Navigating Hospital Isolation Zones for Disease Control

Comparative Analysis of Artificial Intelligence Virtual Assistant and Large Language Models in Post-Operative Care

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