Perception of Chat Generative Pre-trained Transformer (Chat-GPT) AI tool amongst MSK clinicians

Attitude and utilization of ChatGPT among registered nurses: A cross-sectional study

AIM

This study explores the influencing factors of attitudes and behaviors toward use of ChatGPT based on the Technology Acceptance Model among registered nurses in Taiwan.

BACKGROUND

The complexity of medical services and nursing shortages increases workloads. ChatGPT swiftly answers medical questions, provides clinical guidelines, and assists with patient information management, thereby improving nursing efficiency.

INTRODUCTION

To facilitate the development of effective ChatGPT training programs, it is essential to examine registered nurses' attitudes toward and utilization of ChatGPT across diverse workplace settings.

METHODS

An anonymous online survey was used to collect data from over 1000 registered nurses recruited through social media platforms between November 2023 and January 2024. Descriptive statistics and multiple linear regression analyses were conducted for data analysis.

RESULTS

Among respondents, some were unfamiliar with ChatGPT, while others had used it before, with higher usage among males, higher-educated individuals, experienced nurses, and supervisors. Gender and work settings influenced perceived risks, and those familiar with ChatGPT recognized its social impact. Perceived risk and usefulness significantly influenced its adoption.

DISCUSSION

Nurse attitudes to ChatGPT vary based on gender, education, experience, and role. Positive perceptions emphasize its usefulness, while risk concerns affect adoption. The insignificant role of perceived ease of use highlights ChatGPT's user-friendly nature.

CONCLUSION

Over half of the surveyed nurses had used or were familiar with ChatGPT and showed positive attitudes toward its use. Establishing rigorous guidelines to enhance their interaction with ChatGPT is crucial for future training.

IMPLICATIONS FOR NURSING AND HEALTH POLICY

Nurse managers should understand registered nurses' attitudes toward ChatGPT and integrate it into in-service education with tailored support and training, including appropriate prompt formulation and advanced decision-making, to prevent misuse.

Generative Artificial Intelligence and Musculoskeletal Health Care

Generative artificial intelligence (AI) comprises a class of AI models that generate synthetic outputs based on learning acquired from a dataset that trained the model. This means that they can create entirely new outputs that resemble real-world data despite not being explicitly instructed to do so during training. Regarding technological capabilities, computing power, and data availability, generative AI has given rise to more advanced and versatile models including diffusion and large language models that hold promise in healthcare. In musculoskeletal healthcare, generative AI applications may involve the enhancement of images, generation of audio and video, automation of clinical documentation and administrative tasks, use of surgical planning aids, augmentation of treatment decisions, and personalization of patient communication. Limitations of the use of generative AI in healthcare include hallucinations, model bias, ethical considerations during clinical use, knowledge gaps, and lack of transparency. This review introduces critical concepts of generative AI, presents clinical applications relevant to musculoskeletal healthcare that are in development, and highlights limitations preventing deployment in clinical settings.

Health profession students' perceptions of ChatGPT in healthcare and education: insights from a mixed-methods study

OBJECTIVE

The aim of this study was to investigate the perceptions of health profession students regarding ChatGPT use and the potential impact of integrating ChatGPT in healthcare and education.

BACKGROUND

Artificial Intelligence is increasingly utilized in medical education and clinical profession training. However, since its introduction, ChatGPT remains relatively unexplored in terms of health profession students' acceptance of its use in education and practice.

DESIGN

This study employed a mixed-methods approach, using a web-based survey.

METHODS

The study involved a convenience sample recruited through various methods, including Faculty of Medicine announcements, social media, and snowball sampling, during the second semester (March to June 2023). Data were collected using a structured questionnaire with closed-ended questions and three open-ended questions. The final sample comprised 217 undergraduate health profession students, including 73 (33.6%) nursing students, 65 (30.0%) medical students, and 79 (36.4%) occupational therapy, physiotherapy, and speech therapy students.

RESULTS

Among the surveyed students, 86.2% were familiar with ChatGPT, with generally positive perceptions as reflected by a mean score of 4.04 (SD = 0.62) on a scale of 1 to 5. Positive feedback was particularly noted with respect to ChatGPT's role in information retrieval and summarization. The qualitative data revealed three main themes: experiences with ChatGPT, its impact on the quality of healthcare, and its integration into the curriculum. The findings highlight benefits such as serving as a convenient tool for accessing information, reducing human errors, and fostering innovative learning approaches. However, they also underscore areas of concern, including ethical considerations, challenges in fostering critical thinking, and issues related to verification. The absence of significant differences between the different fields of study indicates consistent perceptions across nursing, medicine, and other health profession students.

CONCLUSIONS

Our findings underscore the necessity for continuous refinement to enhance ChatGPT's accuracy, reliability, and alignment with the diverse educational needs of health professions. These insights not only deepen our understanding of student perceptions of ChatGPT in healthcare education but also have significant implications for the future integration of AI in health profession practice. The study emphasizes the importance of a careful balance between leveraging the benefits of AI tools and addressing ethical and pedagogical concerns.

Evaluation of the Impact of ChatGPT on the Selection of Surgical Technique in Bariatric Surgery

PURPOSE

With the growing interest in artificial intelligence (AI) applications in medicine, this study explores ChatGPT's potential to influence surgical technique selection in metabolic and bariatric surgery (MBS), contrasting AI recommendations with established clinical guidelines and expert consensus.

MATERIALS AND METHODS

Conducting a single-center retrospective analysis, the study involved 161 patients who underwent MBS between January 2022 and December 2023. ChatGPT4 was used to analyze patient data, including demographics, pathological history, and BMI, to recommend the most suitable surgical technique. These AI recommendations were then compared with the hospital's algorithm-based decisions.

RESULTS

ChatGPT recommended Roux-en-Y gastric bypass in over half of the cases. However, a significant difference was observed between AI suggestions and actual surgical techniques applied, with only a 34.16% match rate. Further analysis revealed any significant correlation between ChatGPT recommendations and the established surgical algorithm.

CONCLUSION

Despite ChatGPT's ability to process and analyze large datasets, its recommendations for MBS techniques do not align closely with those determined by expert surgical teams using a high success rate algorithm. Consequently, the study concludes that ChatGPT4 should not replace expert consultation in selecting MBS techniques.

Impact of artificial intelligence in managing musculoskeletal pathologies in physiatry: a qualitative observational study evaluating the potential use of ChatGPT versus Copilot for patient information and clinical advice on low back pain

BACKGROUND

The self-management of low back pain (LBP) through patient information interventions offers significant benefits in terms of cost, reduced work absenteeism, and overall healthcare utilization. Using a large language model (LLM), such as ChatGPT (OpenAI) or Copilot (Microsoft), could potentially enhance these outcomes further. Thus, it is important to evaluate the LLMs ChatGPT and Copilot in providing medical advice for LBP and assessing the impact of clinical context on the quality of responses.

METHODS

This was a qualitative comparative observational study. It was conducted within the Department of Physical Medicine and Rehabilitation, University of Montreal in Montreal, QC, Canada. ChatGPT and Copilot were used to answer 27 common questions related to LBP, with and without a specific clinical context. The responses were evaluated by physiatrists for validity, safety, and usefulness using a 4-point Likert scale (4, most favorable).

RESULTS

Both ChatGPT and Copilot demonstrated good performance across all measures. Validity scores were 3.33 for ChatGPT and 3.18 for Copilot, safety scores were 3.19 for ChatGPT and 3.13 for Copilot, and usefulness scores were 3.60 for ChatGPT and 3.57 for Copilot. The inclusion of clinical context did not significantly change the results.

CONCLUSION

LLMs, such as ChatGPT and Copilot, can provide reliable medical advice on LBP, irrespective of the detailed clinical context, supporting their potential to aid in patient self-management.

ChatGPT Use Among Pediatric Health Care Providers: Cross-Sectional Survey Study

BACKGROUND

The public launch of OpenAI's ChatGPT platform generated immediate interest in the use of large language models (LLMs). Health care institutions are now grappling with establishing policies and guidelines for the use of these technologies, yet little is known about how health care providers view LLMs in medical settings. Moreover, there are no studies assessing how pediatric providers are adopting these readily accessible tools.

OBJECTIVE

The aim of this study was to determine how pediatric providers are currently using LLMs in their work as well as their interest in using a Health Insurance Portability and Accountability Act (HIPAA)-compliant version of ChatGPT in the future.

METHODS

A survey instrument consisting of structured and unstructured questions was iteratively developed by a team of informaticians from various pediatric specialties. The survey was sent via Research Electronic Data Capture (REDCap) to all Boston Children's Hospital pediatric providers. Participation was voluntary and uncompensated, and all survey responses were anonymous.

RESULTS

Surveys were completed by 390 pediatric providers. Approximately 50% (197/390) of respondents had used an LLM; of these, almost 75% (142/197) were already using an LLM for nonclinical work and 27% (52/195) for clinical work. Providers detailed the various ways they are currently using an LLM in their clinical and nonclinical work. Only 29% (n=105) of 362 respondents indicated that ChatGPT should be used for patient care in its present state; however, 73.8% (273/368) reported they would use a HIPAA-compliant version of ChatGPT if one were available. Providers' proposed future uses of LLMs in health care are described.

CONCLUSIONS

Despite significant concerns and barriers to LLM use in health care, pediatric providers are already using LLMs at work. This study will give policy makers needed information about how providers are using LLMs clinically.

ChatGPT in the development of medical questionnaires. The example of the low back pain

In the last year, Chat Generative Pre-Trained Transformer (ChatGPT), a web software based on artificial intelligence has been showing high potential in every field of knowledge. In the medical area, its possible application is an object of many studies with promising results. We performed the current study to investigate the possible usefulness of ChatGPT in assessing low back pain. We asked ChatGPT to generate a questionnaire about this clinical condition and we compared the obtained questions and results with the ones obtained by other validated questionnaires: Oswestry Disability Index, Quebec Back Pain Disability Scale, Roland-Morris Disability Questionnaire, and Numeric Rating Scale for pain. We enrolled 20 subjects with low back pain and we found important consistencies among the validated questionnaires. The ChatGPT questionnaire showed an acceptable significant correlation only with Oswestry Disability Index and Quebec Back Pain Disability Scale. ChatGPT showed some peculiarities, especially in the assessment of quality of life and medical consultation and treatments. Our study shows that ChatGPT can help evaluate patients, including multilevel perspectives. However, its power is limited, and further research and validation are required.