Enhancing Awareness and Self-diagnosis of Obstructive Sleep Apnea Using AI-Powered Chatbots: The Role of ChatGPT in Revolutionizing Healthcare

Bilal, Maham; Jamil, Yumna; Rana, Deepshikha; Shah, Hussain Haider

doi:10.1007/s10439-023-03298-8

Cited by 8 publications

(4 citation statements)

References 12 publications

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“… 3 Their versatility extends to various clinical applications, 4 including supporting clinical decision-making, 5 , 6 , 7 generating medical documentation, 8 , 9 , 10 and assisting in diagnosis. 11 , 12 , 13 , 14 , 15 , 16 …”

Section: Introductionmentioning

confidence: 99%

Popular large language model chatbots’ accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries

Pushpanathan,

Lim,

Er Yew

et al. 2023

iScience

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Section: Introductionmentioning

confidence: 99%

Popular large language model chatbots’ accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries

Pushpanathan,

Lim,

Er Yew

et al. 2023

iScience

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“…The dual nature of technology (enabling academic dishonesty while potentially enriching pedagogical approaches) forces institutions to critically evaluate their assessment methodologies to ensure content accuracy and authenticity [22,23]. The foray of generative AI into the educational space has motivated academics to rethink traditional educational frameworks, generating opportunities and challenges [24,25]. In summary, the integration of generative artificial intelligence at the professional and academic levels requires rigorous evaluation, continuous research, and adaptation strategies to take advantage of its advantages and address the associated challenges [22][23][24][25].…”

Section: Discussion About Chatgptmentioning

confidence: 99%

“…The foray of generative AI into the educational space has motivated academics to rethink traditional educational frameworks, generating opportunities and challenges [24,25]. In summary, the integration of generative artificial intelligence at the professional and academic levels requires rigorous evaluation, continuous research, and adaptation strategies to take advantage of its advantages and address the associated challenges [22][23][24][25].…”

Section: Discussion About Chatgptmentioning

confidence: 99%

Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering

Velásquez-Henao,

Franco-Cardona,

Cadavid-Higuita

2023

DYNA

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ChatGPT is a versatile conversational Artificial Intelligence model that responds to user input prompts, with applications in academia and various sectors. However, crafting effective prompts can be challenging, leading to potentially inaccurate or contextually inappropriate responses, emphasizing the importance of prompt engineering in achieving accurate outcomes across different domains. This study aims to address this void by introducing a methodology for optimizing interactions with Artificial Intelligence language models, like ChatGPT, through prompts in the field of engineering. The approach is called GPEI and relies on the latest advancements in this area; and consists of four steps: define the objective, design the prompt, evaluate the response, and iterate. Our proposal involves two key aspects: data inclusion in prompt design for engineering applications and the integration of Explainable Artificial Intelligence principles to assess responses, enhancing transparency. It combines insights from various methodologies to address issues like hallucinations, emphasizing iterative prompt refinement techniques like posing opposing questions and using specific patterns for improvement. This methodology could improve prompt precision and utility in engineering.

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“…There are only a few studies that have assessed sleep-related knowledge of conversational AI-based chatbots, such as ChatGPT-4, which was found very recently to successfully pass the sleep medicine certification board examinations [ 13 ] and be conversant in sleep disorders, such as obstructive sleep apnea syndrome [ 14 - 16 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis

Bragazzi,

Garbarino

2024

JMIR Form Res

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Background Adequate sleep is essential for maintaining individual and public health, positively affecting cognition and well-being, and reducing chronic disease risks. It plays a significant role in driving the economy, public safety, and managing health care costs. Digital tools, including websites, sleep trackers, and apps, are key in promoting sleep health education. Conversational artificial intelligence (AI) such as ChatGPT (OpenAI, Microsoft Corp) offers accessible, personalized advice on sleep health but raises concerns about potential misinformation. This underscores the importance of ensuring that AI-driven sleep health information is accurate, given its significant impact on individual and public health, and the spread of sleep-related myths. Objective This study aims to examine ChatGPT’s capability to debunk sleep-related disbeliefs. Methods A mixed methods design was leveraged. ChatGPT categorized 20 sleep-related myths identified by 10 sleep experts and rated them in terms of falseness and public health significance, on a 5-point Likert scale. Sensitivity, positive predictive value, and interrater agreement were also calculated. A qualitative comparative analysis was also conducted. Results ChatGPT labeled a significant portion (n=17, 85%) of the statements as “false” (n=9, 45%) or “generally false” (n=8, 40%), with varying accuracy across different domains. For instance, it correctly identified most myths about “sleep timing,” “sleep duration,” and “behaviors during sleep,” while it had varying degrees of success with other categories such as “pre-sleep behaviors” and “brain function and sleep.” ChatGPT’s assessment of the degree of falseness and public health significance, on the 5-point Likert scale, revealed an average score of 3.45 (SD 0.87) and 3.15 (SD 0.99), respectively, indicating a good level of accuracy in identifying the falseness of statements and a good understanding of their impact on public health. The AI-based tool showed a sensitivity of 85% and a positive predictive value of 100%. Overall, this indicates that when ChatGPT labels a statement as false, it is highly reliable, but it may miss identifying some false statements. When comparing with expert ratings, high intraclass correlation coefficients (ICCs) between ChatGPT’s appraisals and expert opinions could be found, suggesting that the AI’s ratings were generally aligned with expert views on falseness (ICC=.83, P<.001) and public health significance (ICC=.79, P=.001) of sleep-related myths. Qualitatively, both ChatGPT and sleep experts refuted sleep-related misconceptions. However, ChatGPT adopted a more accessible style and provided a more generalized view, focusing on broad concepts, while experts sometimes used technical jargon, providing evidence-based explanations. Conclusions ChatGPT-4 can accurately address sleep-related queries and debunk sleep-related myths, with a performance comparable to sleep experts, even if, given its limitations, the AI cannot completely replace expert opinions, especially in nuanced and complex fields such as sleep health, but can be a valuable complement in the dissemination of updated information and promotion of healthy behaviors.

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Enhancing Awareness and Self-diagnosis of Obstructive Sleep Apnea Using AI-Powered Chatbots: The Role of ChatGPT in Revolutionizing Healthcare

Cited by 8 publications

References 12 publications

Popular large language model chatbots’ accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries

Popular large language model chatbots’ accuracy, comprehensiveness, and self-awareness in answering ocular symptom queries

Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering

Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis

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