The appropriation of conversational AI in the workplace: A taxonomy of AI chatbot users

Assessing the accuracy and reproducibility of ChatGPT for responding to patient inquiries about otosclerosis

BACKGROUND

Patients increasingly use chatbots powered by artificial intelligence to seek information. However, there is a lack of reliable studies on the accuracy and reproducibility of the information provided by these models. Therefore, we conducted a study investigating the ChatGPT's responses to questions about otosclerosis.

METHODS

96 otosclerosis-related questions were collected from internet searches and websites of professional institutions and societies. Questions are divided into four sub-categories. These questions were directed at the latest version of ChatGPT Plus, and these responses were assessed by two otorhinolaryngology surgeons. Accuracy was graded as correct, incomplete, mixed, and irrelevant. Reproducibility was evaluated by comparing the consistency of the two answers to each specific question.

RESULTS

The overall accuracy and reproducibility rates of GPT-4o for correct answers were found to be 64.60% and 89.60%, respectively. The findings showed correct answers for accuracy and reproducibility for basic knowledge were 64.70% and 91.20%; for diagnosis & management, 64.0% and 92.0%; for medical & surgical treatment, 52.95% and 82.35%; and for operative risks & postoperative period, 75.0% and 90.0%, respectively. There were no significant differences found between the answers and groups in terms of accuracy and reproducibility (p = 0.073 and p = 0.752, respectively).

CONCLUSION

GPT-4o achieved satisfactory accuracy results, except in the diagnosis & management and medical & surgical treatment categories. Reproducibility was generally high across all categories. With the audio and visual communication capabilities of GPT-4o, under the supervision of a medical professional, this model can be utilized to provide medical information and support for otosclerosis patients.

Evaluating the Performance of ChatGPT, Gemini, and Bing Compared with Resident Surgeons in the Otorhinolaryngology In-service Training Examination

Objective

Large language models (LLMs) are used in various fields for their ability to produce human-like text. They are particularly useful in medical education, aiding clinical management skills and exam preparation for residents. To evaluate and compare the performance of ChatGPT (GPT-4), Gemini, and Bing with each other and with otorhinolaryngology residents in answering in-service training exam questions and provide insights into the usefulness of these models in medical education and healthcare.

Methods

Eight otorhinolaryngology in-service training exams were used for comparison. 316 questions were prepared from the Resident Training Textbook of the Turkish Society of Otorhinolaryngology Head and Neck Surgery. These questions were presented to the three artificial intelligence models. The exam results were evaluated to determine the accuracy of both models and residents.

Results

GPT-4 achieved the highest accuracy among the LLMs at 54.75% (GPT-4 vs. Gemini p=0.002, GPT-4 vs. Bing p<0.001), followed by Gemini at 40.50% and Bing at 37.00% (Gemini vs. Bing p=0.327). However, senior residents outperformed all LLMs and other residents with an accuracy rate of 75.5% (p<0.001). The LLMs could only compete with junior residents. GPT- 4 and Gemini performed similarly to juniors, whose accuracy level was 46.90% (p=0.058 and p=0.120, respectively). However, juniors still outperformed Bing (p=0.019).

Conclusion

The LLMs currently have limitations in achieving the same medical accuracy as senior and mid-level residents. However, they outperform in specific subspecialties, indicating the potential usefulness in certain medical fields.

Understanding Users' Acceptance of Artificial Intelligence Applications: A Literature Review

In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users' acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human-AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users' acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research.

Comparison of ChatGPT-3.5, ChatGPT-4, and Orthopaedic Resident Performance on Orthopaedic Assessment Examinations

INTRODUCTION

Artificial intelligence (AI) programs have the ability to answer complex queries including medical profession examination questions. The purpose of this study was to compare the performance of orthopaedic residents (ortho residents) against Chat Generative Pretrained Transformer (ChatGPT)-3.5 and GPT-4 on orthopaedic assessment examinations. A secondary objective was to perform a subgroup analysis comparing the performance of each group on questions that included image interpretation versus text-only questions.

METHODS

The ResStudy orthopaedic examination question bank was used as the primary source of questions. One hundred eighty questions and answer choices from nine different orthopaedic subspecialties were directly input into ChatGPT-3.5 and then GPT-4. ChatGPT did not have consistently available image interpretation, so no images were directly provided to either AI format. Answers were recorded as correct versus incorrect by the chatbot, and resident performance was recorded based on user data provided by ResStudy.

RESULTS

Overall, ChatGPT-3.5, GPT-4, and ortho residents scored 29.4%, 47.2%, and 74.2%, respectively. There was a difference among the three groups in testing success, with ortho residents scoring higher than ChatGPT-3.5 and GPT-4 ( P < 0.001 and P < 0.001). GPT-4 scored higher than ChatGPT-3.5 ( P = 0.002). A subgroup analysis was performed by dividing questions into question stems without images and question stems with images. ChatGPT-3.5 was more correct (37.8% vs. 22.4%, respectively, OR = 2.1, P = 0.033) and ChatGPT-4 was also more correct (61.0% vs. 35.7%, OR = 2.8, P < 0.001), when comparing text-only questions versus questions with images. Residents were 72.6% versus 75.5% correct with text-only questions versus questions with images, with no significant difference ( P = 0.302).

CONCLUSION

Orthopaedic residents were able to answer more questions accurately than ChatGPT-3.5 and GPT-4 on orthopaedic assessment examinations. GPT-4 is superior to ChatGPT-3.5 for answering orthopaedic resident assessment examination questions. Both ChatGPT-3.5 and GPT-4 performed better on text-only questions than questions with images. It is unlikely that GPT-4 or ChatGPT-3.5 would pass the American Board of Orthopaedic Surgery written examination.