Predicting medical specialty from text based on a domain-specific pre-trained BERT

Kim, Yoojoong; Kim, Jong-Ho; Kim, Young‐Min; Song, Sanghoun; Joo, Hyung Joon

doi:10.1016/j.ijmedinf.2022.104956

Cited by 19 publications

(10 citation statements)

References 16 publications

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“…As stated by Gabrielson et al [ 11 ], they may serve as an essential tool in the urologist’s armamentarium to step away from the computer and turn the physician’s chair back toward the patient. Kim et al [ 24 ] propose a medical specialty prediction model from patient-side medical question text based on pre-trained bidirectional encoder representations from transformers (BERT). The dataset comprised pairs of medical question texts and labeled specialties scraped from a website for the medical question-and-answer service.…”

Section: Resultsmentioning

confidence: 99%

Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care Through Artificial Intelligence

Talyshinskii,

Naik,

Hameed

et al. 2023

Curr Urol Rep

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Purpose of Review Artificial intelligence (AI) chatbots have emerged as a potential tool to transform urology by improving patient care and physician efficiency. With an emphasis on their potential advantages and drawbacks, this literature review offers a thorough assessment of the state of AI-driven chatbots in urology today. Recent Findings The capacity of AI-driven chatbots in urology to give patients individualized and timely medical advice is one of its key advantages. Chatbots can help patients prioritize their symptoms and give advice on the best course of treatment. By automating administrative duties and offering clinical decision support, chatbots can also help healthcare providers. Before chatbots are widely used in urology, there are a few issues that need to be resolved. The precision of chatbot diagnoses and recommendations might be impacted by technical constraints like system errors and flaws. Additionally, issues regarding the security and privacy of patient data must be resolved, and chatbots must adhere to all applicable laws. Important issues that must be addressed include accuracy and dependability because any mistakes or inaccuracies could seriously harm patients. The final obstacle is resistance from patients and healthcare professionals who are hesitant to use new technology or who value in-person encounters. Summary AI-driven chatbots have the potential to significantly improve urology care and efficiency. However, it is essential to thoroughly test and ensure the accuracy of chatbots, address privacy and security concerns, and design user-friendly chatbots that can integrate into existing workflows. By exploring various scenarios and examining the current literature, this review provides an analysis of the prospects and limitations of implementing chatbots in urology.

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Section: Resultsmentioning

confidence: 99%

Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care Through Artificial Intelligence

Talyshinskii,

Naik,

Hameed

et al. 2023

Curr Urol Rep

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“…Kim et al . [ 4 ] studied the prediction of medical specialties via chatbot. These authors found that a single prediction yielded an accuracy of 70.6%, while three predictions showed a higher accuracy of 88.5%.…”

Section: Discussionmentioning

confidence: 99%

“…[ 2 3 ] These systems, which have been used for user support and consultancy on many online platforms for years, have recently been introduced in the academic community in the field of health care. [ 4 5 6 ]…”

Section: Introductionmentioning

confidence: 99%

Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT)

Sarbay,

Berikol,

Özturan

2023

Turkish Journal of Emergency Medicine

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OBJECTIVES: Artificial intelligence companies have been increasing their initiatives recently to improve the results of chatbots, which are software programs that can converse with a human in natural language. The role of chatbots in health care is deemed worthy of research. OpenAI's ChatGPT is a supervised and empowered machine learning-based chatbot. The aim of this study was to determine the performance of ChatGPT in emergency medicine (EM) triage prediction. METHODS: This was a preliminary, cross-sectional study conducted with case scenarios generated by the researchers based on the emergency severity index (ESI) handbook v4 cases. Two independent EM specialists who were experts in the ESI triage scale determined the triage categories for each case. A third independent EM specialist was consulted as arbiter, if necessary. Consensus results for each case scenario were assumed as the reference triage category. Subsequently, each case scenario was queried with ChatGPT and the answer was recorded as the index triage category. Inconsistent classifications between the ChatGPT and reference category were defined as over-triage (false positive) or under-triage (false negative). RESULTS: Fifty case scenarios were assessed in the study. Reliability analysis showed a fair agreement between EM specialists and ChatGPT (Cohen's Kappa: 0.341). Eleven cases (22%) were over triaged and 9 (18%) cases were under triaged by ChatGPT. In 9 cases (18%), ChatGPT reported two consecutive triage categories, one of which matched the expert consensus. It had an overall sensitivity of 57.1% (95% confidence interval [CI]: 34–78.2), specificity of 34.5% (95% CI: 17.9–54.3), positive predictive value (PPV) of 38.7% (95% CI: 21.8–57.8), negative predictive value (NPV) of 52.6 (95% CI: 28.9–75.6), and an F1 score of 0.461. In high acuity cases (ESI-1 and ESI-2), ChatGPT showed a sensitivity of 76.2% (95% CI: 52.8–91.8), specificity of 93.1% (95% CI: 77.2–99.2), PPV of 88.9% (95% CI: 65.3–98.6), NPV of 84.4 (95% CI: 67.2–94.7), and an F1 score of 0.821. The receiver operating characteristic curve showed an area under the curve of 0.846 (95% CI: 0.724–0.969, P < 0.001) for high acuity cases. CONCLUSION: The performance of ChatGPT was best when predicting high acuity cases (ESI-1 and ESI-2). It may be useful when determining the cases requiring critical care. When trained with more medical knowledge, ChatGPT may be more accurate for other triage category predictions.

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“…Bots are revolutionizing healthcare to the extent that they can provide benefits to management, patient conditions [282], hospitals [264], drug prescribing [10], although the justification of eligibility rests entirely with doctors and medics by enforcing data security and confidentiality protocols. It is imperative to have robust data protection measures in place to protect patient information and comply with relevant regulations [204], [285].…”

Section: Chatbot For Healthcarementioning

confidence: 99%

“…Between Predicting medical specialty from text based on a domain-specific pre-trained BERT and ChatGPT and the rise of generative AI. Additionally, predicting medical specialty from text based on a domain-specific pre-trained BERT [264]. Development and validation of deep learning and BERT models for classification of lung cancer radiology reports [274].…”

Section: Fig 5 Ai Components For Nlpmentioning

confidence: 99%

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Suryanto,

Wibawa,

Hariyono

et al. 2023

IJRCS

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Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.

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Predicting medical specialty from text based on a domain-specific pre-trained BERT

Cited by 19 publications

References 16 publications

Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care Through Artificial Intelligence

Potential of AI-Driven Chatbots in Urology: Revolutionizing Patient Care Through Artificial Intelligence

Performance of emergency triage prediction of an open access natural language processing based chatbot application (ChatGPT)

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

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