Non-rigid Multi-Modal Medical Image Registration Based on Improved Maximum Mutual Information PV Image Interpolation Method

He, Liting

doi:10.3389/fpubh.2022.863307

Cited by 3 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…9. Most AI-assisted arthroscopic surgeries require additional incisions for fixing reference frames, increasing patient trauma [ 55 ]. 10.…”

Section: Limitations and Challenges Of Ai And Ar In Orthopaedic Arthr...mentioning

confidence: 99%

Application progress of artificial intelligence and augmented reality in orthopaedic arthroscopy surgery

Chen

2023

J Orthop Surg Res

View full text Add to dashboard Cite

In today's rapidly developing technological era, the technological revolution triggered by the rapid iteration of artificial intelligence and augmented reality has provided brand-new digital intelligent empowerment for orthopaedic clinical operation. Although traditional arthroscopy has been widely promoted globally due to its advantages such as minimally invasive, safety and early functional exercise, it still has deficiencies in precision and personalization. The assistance of artificial intelligence and augmented reality enables precise positioning and navigation in arthroscopic surgery, as well as personalized operations based on patient conditions, which lifts the objective limitations of traditional sports medicine surgery. The integration of artificial intelligence and augmented reality with orthopaedic arthroscopy surgery is still in infancy, even though there are still some insufficient to be solved, but its prospect is bright.

show abstract

“…9. Most AI-assisted arthroscopic surgeries require additional incisions for fixing reference frames, increasing patient trauma [ 55 ]. 10.…”

Section: Limitations and Challenges Of Ai And Ar In Orthopaedic Arthr...mentioning

confidence: 99%

Application progress of artificial intelligence and augmented reality in orthopaedic arthroscopy surgery

Chen

2023

J Orthop Surg Res

View full text Add to dashboard Cite

show abstract

Physician Versus Large Language Model Chatbot Responses to Web-Based Questions From Autistic Patients in Chinese: Cross-Sectional Comparative Analysis

He,

Zhang,

Jin

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background There is a dearth of feasibility assessments regarding using large language models (LLMs) for responding to inquiries from autistic patients within a Chinese-language context. Despite Chinese being one of the most widely spoken languages globally, the predominant research focus on applying these models in the medical field has been on English-speaking populations. Objective This study aims to assess the effectiveness of LLM chatbots, specifically ChatGPT-4 (OpenAI) and ERNIE Bot (version 2.2.3; Baidu, Inc), one of the most advanced LLMs in China, in addressing inquiries from autistic individuals in a Chinese setting. Methods For this study, we gathered data from DXY—a widely acknowledged, web-based, medical consultation platform in China with a user base of over 100 million individuals. A total of 100 patient consultation samples were rigorously selected from January 2018 to August 2023, amounting to 239 questions extracted from publicly available autism-related documents on the platform. To maintain objectivity, both the original questions and responses were anonymized and randomized. An evaluation team of 3 chief physicians assessed the responses across 4 dimensions: relevance, accuracy, usefulness, and empathy. The team completed 717 evaluations. The team initially identified the best response and then used a Likert scale with 5 response categories to gauge the responses, each representing a distinct level of quality. Finally, we compared the responses collected from different sources. Results Among the 717 evaluations conducted, 46.86% (95% CI 43.21%-50.51%) of assessors displayed varying preferences for responses from physicians, with 34.87% (95% CI 31.38%-38.36%) of assessors favoring ChatGPT and 18.27% (95% CI 15.44%-21.10%) of assessors favoring ERNIE Bot. The average relevance scores for physicians, ChatGPT, and ERNIE Bot were 3.75 (95% CI 3.69-3.82), 3.69 (95% CI 3.63-3.74), and 3.41 (95% CI 3.35-3.46), respectively. Physicians (3.66, 95% CI 3.60-3.73) and ChatGPT (3.73, 95% CI 3.69-3.77) demonstrated higher accuracy ratings compared to ERNIE Bot (3.52, 95% CI 3.47-3.57). In terms of usefulness scores, physicians (3.54, 95% CI 3.47-3.62) received higher ratings than ChatGPT (3.40, 95% CI 3.34-3.47) and ERNIE Bot (3.05, 95% CI 2.99-3.12). Finally, concerning the empathy dimension, ChatGPT (3.64, 95% CI 3.57-3.71) outperformed physicians (3.13, 95% CI 3.04-3.21) and ERNIE Bot (3.11, 95% CI 3.04-3.18). Conclusions In this cross-sectional study, physicians’ responses exhibited superiority in the present Chinese-language context. Nonetheless, LLMs can provide valuable medical guidance to autistic patients and may even surpass physicians in demonstrating empathy. However, it is crucial to acknowledge that further optimization and research are imperative prerequisites before the effective integration of LLMs in clinical settings across diverse linguistic environments can be realized. Trial Registration Chinese Clinical Trial Registry ChiCTR2300074655; https://www.chictr.org.cn/bin/project/edit?pid=199432

show abstract

Physician Versus Large Language Model Chatbot Responses to Web-Based Questions From Autistic Patients in Chinese: Cross-Sectional Comparative Analysis (Preprint)

He,

Zhang,

Jin

et al. 2023

Preprint

View full text Add to dashboard Cite

BACKGROUND There is a dearth of Feasibility assessments regarding the utilization of large language models for responding to inquiries from autistic patients within a Chinese-language context. Despite Chinese being one of the most widely spoken languages globally, the predominant focus of research on the application of these models in the medical field has been on English-speaking populations. OBJECTIVE To assess the effectiveness of LLM chatbots, specifically ChatGPT and ERNIE Bot, in addressing inquiries from individuals with autism in a Chinese setting. METHODS A total of 100 patient consultation samples were randomly selected from publicly available autism-related records on DXY, spanning the period from January 2018 to August 2023 and including 239 questions. To maintain objectivity, both the original questions and responses were anonymized and randomized. An evaluation team, consisting of three chief physicians, assessed the responses across four dimensions: relevance, accuracy, usefulness, and empathy. In total, 717 evaluations were conducted. The team initially identified the best response and then employed a Likert scale with five response categories to gauge the responses, each representing a distinct level of quality. Finally, a comparative analysis was conducted to compare the responses obtained from various sources. RESULTS Among the 717 evaluations conducted, 46.86% (95% CI, 43.21%–50.51%) of assessors displayed varying preferences for responses from physicians, with 34.87% (95% CI, 31.38%–38.36%) favoring ChatGPT and 18.27% (95% CI, 15.44%–21.10%) favoring ERNIE Bot. The average relevance scores for physicians, ChatGPT, and ERNIE Bot were 3.75 (95% CI, 3.69–3.82), 3.69 (95% CI, 3.63–3.74), and 3.41 (95% CI, 3.35–3.46), respectively. Regarding accuracy ratings, physicians (3.66, 95% CI, 3.60–3.73) and ChatGPT (3.73, 95% CI, 3.69–3.77) outperformed ERNIE Bot (3.52, 95%CI, 3.47–3.57). In terms of usefulness scores, physicians (3.54, 95% CI, 3.47–3.62) received higher ratings than ChatGPT (3.40, 95% CI, 3.34–3.47) and ERNIE Bot (3.05, 95% CI, 2.99–3.12). Finally, concerning the empathy dimension, ChatGPT (3.64, 95% CI, 3.57–3.71) outperformed physicians (3.13, 95% CI, 3.04–3.21) and ERNIE Bot (3.11, 95% CI, 3.04–3.18). CONCLUSIONS In this cross-sectional study, physicians' responses exhibited overall superiority in the present Chinese language context. Nonetheless, LLMs can provide valuable medical guidance to patients with autism and may even surpass physicians in terms of demonstrating empathy. However, it is crucial to acknowledge that further optimization and research are imperative prerequisites before the effective integration of LLMs in clinical settings across diverse linguistic environments can be realized. CLINICALTRIAL The study was registered on chictr.org (ChiCTR2300074655)

show abstract

Non-rigid Multi-Modal Medical Image Registration Based on Improved Maximum Mutual Information PV Image Interpolation Method

Cited by 3 publications

References 36 publications

Application progress of artificial intelligence and augmented reality in orthopaedic arthroscopy surgery

Application progress of artificial intelligence and augmented reality in orthopaedic arthroscopy surgery

Physician Versus Large Language Model Chatbot Responses to Web-Based Questions From Autistic Patients in Chinese: Cross-Sectional Comparative Analysis

Physician Versus Large Language Model Chatbot Responses to Web-Based Questions From Autistic Patients in Chinese: Cross-Sectional Comparative Analysis (Preprint)

Contact Info

Product

Resources

About