Utility of ChatGPT in Clinical Practice

Beyond the Scalpel: Assessing ChatGPT's potential as an auxiliary intelligent virtual assistant in oral surgery

AI has revolutionized the way we interact with technology. Noteworthy advances in AI algorithms and large language models (LLM) have led to the development of natural generative language (NGL) systems such as ChatGPT. Although these LLM can simulate human conversations and generate content in real time, they face challenges related to the topicality and accuracy of the information they generate. This study aimed to assess whether ChatGPT-4 could provide accurate and reliable answers to general dentists in the field of oral surgery, and thus explore its potential as an intelligent virtual assistant in clinical decision making in oral surgery. Thirty questions related to oral surgery were posed to ChatGPT4, each question repeated 30 times. Subsequently, a total of 900 responses were obtained. Two surgeons graded the answers according to the guidelines of the Spanish Society of Oral Surgery, using a three-point Likert scale (correct, partially correct/incomplete, and incorrect). Disagreements were arbitrated by an experienced oral surgeon, who provided the final grade Accuracy was found to be 71.7%, and consistency of the experts' grading across iterations, ranged from moderate to almost perfect. ChatGPT-4, with its potential capabilities, will inevitably be integrated into dental disciplines, including oral surgery. In the future, it could be considered as an auxiliary intelligent virtual assistant, though it would never replace oral surgery experts. Proper training and verified information by experts will remain vital to the implementation of the technology. More comprehensive research is needed to ensure the safe and successful application of AI in oral surgery.

Use and Application of Large Language Models for Patient Questions Following Total Knee Arthroplasty

BACKGROUND

A consumer-focused health care model not only allows unprecedented access to information, but equally warrants consideration of the appropriateness of providing accurate patient health information. Nurses play a large role in influencing patient satisfaction following total knee arthroplasty (TKA), but they come at a cost. A specific natural language artificial intelligence (AI) model, ChatGPT (Chat Generative Pre-trained Transformer), has accumulated over 100 million users within months of launching. As such, we aimed to compare: (1) orthopaedic surgeons' evaluation of the appropriateness of the answers to the most frequently asked patient questions after TKA; and (2) patients' comfort level in answering their postoperative questions by using answers provided by arthroplasty-trained nurses and ChatGPT.

METHODS

We prospectively created 60 questions based on the most commonly asked patient questions following TKA. There were 3 fellowship-trained surgeons who assessed the answers provided by arthroplasty-trained nurses and ChatGPT-4 to each of the questions. The surgeons graded each set of responses based on clinical judgment as: (1) "appropriate," (2) "inappropriate" if the response contained inappropriate information, or (3) "unreliable," if the responses provided inconsistent content. Patients' comfort level and trust in AI were assessed using Research Electronic Data Capture (REDCap) hosted at our local hospital.

RESULTS

The surgeons graded 44 out of 60 (73.3%) responses for the arthroplasty-trained nurses and 44 out of 60 (73.3%) for ChatGPT to be "appropriate." There were 4 responses graded "inappropriate" and one response graded "unreliable" provided by the nurses. For the ChatGPT response, there were 5 responses graded "inappropriate" and no responses graded "unreliable." There were 136 patients (53.8%) who were more comfortable with the answers provided by ChatGPT compared to 86 patients (34.0%) who preferred the answers from arthroplasty-trained nurses. Of the 253 patients, 233 (92.1%) were uncertain if they would trust AI to answer their postoperative questions. There were 127 patients (50.2%) who answered that if they knew the previous answer was provided by ChatGPT, their comfort level in trusting the answer would change.

CONCLUSIONS

One potential use of ChatGPT can be found in providing appropriate answers to patient questions after TKA. At our institution, cost expenditures can potentially be minimized while maintaining patient satisfaction. Inevitably, successful implementation is dependent on the ability to provide information that is credible and in accordance with the objectives of both physicians and patients.

LEVEL OF EVIDENCE

III.

The Comparative Diagnostic Capability of Large Language Models in Otolaryngology

OBJECTIVES

Evaluate and compare the ability of large language models (LLMs) to diagnose various ailments in otolaryngology.

METHODS

We collected all 100 clinical vignettes from the second edition of Otolaryngology Cases-The University of Cincinnati Clinical Portfolio by Pensak et al. With the addition of the prompt "Provide a diagnosis given the following history," we prompted ChatGPT-3.5, Google Bard, and Bing-GPT4 to provide a diagnosis for each vignette. These diagnoses were compared to the portfolio for accuracy and recorded. All queries were run in June 2023.

RESULTS

ChatGPT-3.5 was the most accurate model (89% success rate), followed by Google Bard (82%) and Bing GPT (74%). A chi-squared test revealed a significant difference between the three LLMs in providing correct diagnoses (p = 0.023). Of the 100 vignettes, seven require additional testing results (i.e., biopsy, non-contrast CT) for accurate clinical diagnosis. When omitting these vignettes, the revised success rates were 95.7% for ChatGPT-3.5, 88.17% for Google Bard, and 78.72% for Bing-GPT4 (p = 0.002).

CONCLUSIONS

ChatGPT-3.5 offers the most accurate diagnoses when given established clinical vignettes as compared to Google Bard and Bing-GPT4. LLMs may accurately offer assessments for common otolaryngology conditions but currently require detailed prompt information and critical supervision from clinicians. There is vast potential in the clinical applicability of LLMs; however, practitioners should be wary of possible "hallucinations" and misinformation in responses.

LEVEL OF EVIDENCE

3 Laryngoscope, 134:3997-4002, 2024.

Evaluating the accuracy of ChatGPT-4 in predicting ASA scores: A prospective multicentric study ChatGPT-4 in ASA score prediction

BACKGROUND

This study investigates the potential of ChatGPT-4, developed by OpenAI, in enhancing medical decision-making processes, particularly in preoperative assessments using the American Society of Anesthesiologists (ASA) scoring system. The ASA score, a critical tool in evaluating patients' health status and anesthesia risks before surgery, categorizes patients from I to VI based on their overall health and risk factors. Despite its widespread use, determining accurate ASA scores remains a subjective process that may benefit from AI-supported assessments. This research aims to evaluate ChatGPT-4's capability to predict ASA scores accurately compared to expert anesthesiologists' assessments.

METHODS

In this prospective multicentric study, ethical board approval was obtained, and the study was registered with clinicaltrials.gov (NCT06321445). We included 2851 patients from anesthesiology outpatient clinics, spanning neonates to all age groups and genders, with ASA scores between I-IV. Exclusion criteria were set for ASA V and VI scores, emergency operations, and insufficient information for ASA score determination. Data on patients' demographics, health conditions, and ASA scores by anesthesiologists were collected and anonymized. ChatGPT-4 was then tasked with assigning ASA scores based on the standardized patient data.

RESULTS

Our results indicate a high level of concordance between ChatGPT-4 predictions and anesthesiologists' evaluations, with Cohen's kappa analysis showing a kappa value of 0.858 (p = 0.000). While the model demonstrated over 90% accuracy in predicting ASA scores I to III, it showed a notable variance in ASA IV scores, suggesting a potential limitation in assessing patients with more complex health conditions.

DISCUSSION

The findings suggest that ChatGPT-4 can significantly contribute to the medical field by supporting anesthesiologists in preoperative assessments. This study not only demonstrates ChatGPT-4's efficacy in medical data analysis and decision-making but also opens new avenues for AI applications in healthcare, particularly in enhancing patient safety and optimizing surgical outcomes. Further research is needed to refine AI models for complex case assessments and integrate them seamlessly into clinical workflows.

Understanding Health Care Students' Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study

BACKGROUND

ChatGPT was not intended for use in health care, but it has potential benefits that depend on end-user understanding and acceptability, which is where health care students become crucial. There is still a limited amount of research in this area.

OBJECTIVE

The primary aim of our study was to assess the frequency of ChatGPT use, the perceived level of knowledge, the perceived risks associated with its use, and the ethical issues, as well as attitudes toward the use of ChatGPT in the context of education in the field of health. In addition, we aimed to examine whether there were differences across groups based on demographic variables. The second part of the study aimed to assess the association between the frequency of use, the level of perceived knowledge, the level of risk perception, and the level of perception of ethics as predictive factors for participants' attitudes toward the use of ChatGPT.

METHODS

A cross-sectional survey was conducted from May to June 2023 encompassing students of medicine, nursing, dentistry, nutrition, and laboratory science across the Americas. The study used descriptive analysis, chi-square tests, and ANOVA to assess statistical significance across different categories. The study used several ordinal logistic regression models to analyze the impact of predictive factors (frequency of use, perception of knowledge, perception of risk, and ethics perception scores) on attitude as the dependent variable. The models were adjusted for gender, institution type, major, and country. Stata was used to conduct all the analyses.

RESULTS

Of 2661 health care students, 42.99% (n=1144) were unaware of ChatGPT. The median score of knowledge was "minimal" (median 2.00, IQR 1.00-3.00). Most respondents (median 2.61, IQR 2.11-3.11) regarded ChatGPT as neither ethical nor unethical. Most participants (median 3.89, IQR 3.44-4.34) "somewhat agreed" that ChatGPT (1) benefits health care settings, (2) provides trustworthy data, (3) is a helpful tool for clinical and educational medical information access, and (4) makes the work easier. In total, 70% (7/10) of people used it for homework. As the perceived knowledge of ChatGPT increased, there was a stronger tendency with regard to having a favorable attitude toward ChatGPT. Higher ethical consideration perception ratings increased the likelihood of considering ChatGPT as a source of trustworthy health care information (odds ratio [OR] 1.620, 95% CI 1.498-1.752), beneficial in medical issues (OR 1.495, 95% CI 1.452-1.539), and useful for medical literature (OR 1.494, 95% CI 1.426-1.564; P<.001 for all results).

CONCLUSIONS

Over 40% of American health care students (1144/2661, 42.99%) were unaware of ChatGPT despite its extensive use in the health field. Our data revealed the positive attitudes toward ChatGPT and the desire to learn more about it. Medical educators must explore how chatbots may be included in undergraduate health care education programs.

Performance of Large Language Models in Patient Complaint Resolution: Web-Based Cross-Sectional Survey

BACKGROUND

Patient complaints are a perennial challenge faced by health care institutions globally, requiring extensive time and effort from health care workers. Despite these efforts, patient dissatisfaction remains high. Recent studies on the use of large language models (LLMs) such as the GPT models developed by OpenAI in the health care sector have shown great promise, with the ability to provide more detailed and empathetic responses as compared to physicians. LLMs could potentially be used in responding to patient complaints to improve patient satisfaction and complaint response time.

OBJECTIVE

This study aims to evaluate the performance of LLMs in addressing patient complaints received by a tertiary health care institution, with the goal of enhancing patient satisfaction.

METHODS

Anonymized patient complaint emails and associated responses from the patient relations department were obtained. ChatGPT-4.0 (OpenAI, Inc) was provided with the same complaint email and tasked to generate a response. The complaints and the respective responses were uploaded onto a web-based questionnaire. Respondents were asked to rate both responses on a 10-point Likert scale for 4 items: appropriateness, completeness, empathy, and satisfaction. Participants were also asked to choose a preferred response at the end of each scenario.

RESULTS

There was a total of 188 respondents, of which 115 (61.2%) were health care workers. A majority of the respondents, including both health care and non-health care workers, preferred replies from ChatGPT (n=164, 87.2% to n=183, 97.3%). GPT-4.0 responses were rated higher in all 4 assessed items with all median scores of 8 (IQR 7-9) compared to human responses (appropriateness 5, IQR 3-7; empathy 4, IQR 3-6; quality 5, IQR 3-6; satisfaction 5, IQR 3-6; P<.001) and had higher average word counts as compared to human responses (238 vs 76 words). Regression analyses showed that a higher word count was a statistically significant predictor of higher score in all 4 items, with every 1-word increment resulting in an increase in scores of between 0.015 and 0.019 (all P<.001). However, on subgroup analysis by authorship, this only held true for responses written by patient relations department staff and not those generated by ChatGPT which received consistently high scores irrespective of response length.

CONCLUSIONS

This study provides significant evidence supporting the effectiveness of LLMs in resolution of patient complaints. ChatGPT demonstrated superiority in terms of response appropriateness, empathy, quality, and overall satisfaction when compared against actual human responses to patient complaints. Future research can be done to measure the degree of improvement that artificial intelligence generated responses can bring in terms of time savings, cost-effectiveness, patient satisfaction, and stress reduction for the health care system.

Assessing ChatGPT as a Medical Consultation Assistant for Chronic Hepatitis B: Cross-Language Study of English and Chinese

BACKGROUND

Chronic hepatitis B (CHB) imposes substantial economic and social burdens globally. The management of CHB involves intricate monitoring and adherence challenges, particularly in regions like China, where a high prevalence of CHB intersects with health care resource limitations. This study explores the potential of ChatGPT-3.5, an emerging artificial intelligence (AI) assistant, to address these complexities. With notable capabilities in medical education and practice, ChatGPT-3.5's role is examined in managing CHB, particularly in regions with distinct health care landscapes.

OBJECTIVE

This study aimed to uncover insights into ChatGPT-3.5's potential and limitations in delivering personalized medical consultation assistance for CHB patients across diverse linguistic contexts.

METHODS

Questions sourced from published guidelines, online CHB communities, and search engines in English and Chinese were refined, translated, and compiled into 96 inquiries. Subsequently, these questions were presented to both ChatGPT-3.5 and ChatGPT-4.0 in independent dialogues. The responses were then evaluated by senior physicians, focusing on informativeness, emotional management, consistency across repeated inquiries, and cautionary statements regarding medical advice. Additionally, a true-or-false questionnaire was employed to further discern the variance in information accuracy for closed questions between ChatGPT-3.5 and ChatGPT-4.0.

RESULTS

Over half of the responses (228/370, 61.6%) from ChatGPT-3.5 were considered comprehensive. In contrast, ChatGPT-4.0 exhibited a higher percentage at 74.5% (172/222; P<.001). Notably, superior performance was evident in English, particularly in terms of informativeness and consistency across repeated queries. However, deficiencies were identified in emotional management guidance, with only 3.2% (6/186) in ChatGPT-3.5 and 8.1% (15/154) in ChatGPT-4.0 (P=.04). ChatGPT-3.5 included a disclaimer in 10.8% (24/222) of responses, while ChatGPT-4.0 included a disclaimer in 13.1% (29/222) of responses (P=.46). When responding to true-or-false questions, ChatGPT-4.0 achieved an accuracy rate of 93.3% (168/180), significantly surpassing ChatGPT-3.5's accuracy rate of 65.0% (117/180) (P<.001).

CONCLUSIONS

In this study, ChatGPT demonstrated basic capabilities as a medical consultation assistant for CHB management. The choice of working language for ChatGPT-3.5 was considered a potential factor influencing its performance, particularly in the use of terminology and colloquial language, and this potentially affects its applicability within specific target populations. However, as an updated model, ChatGPT-4.0 exhibits improved information processing capabilities, overcoming the language impact on information accuracy. This suggests that the implications of model advancement on applications need to be considered when selecting large language models as medical consultation assistants. Given that both models performed inadequately in emotional guidance management, this study highlights the importance of providing specific language training and emotional management strategies when deploying ChatGPT for medical purposes. Furthermore, the tendency of these models to use disclaimers in conversations should be further investigated to understand the impact on patients' experiences in practical applications.

Assessment of ChatGPT's adherence to ETA-thyroid nodule management guideline over two different time intervals 14 days apart: in binary and multiple-choice queries

OBJECTIVE

Artificial intelligence (AI) has significant potential in healthcare, particularly in providing decision-support in specialized domains like thyroid nodule management. This study assesses the effectiveness of ChatGPT-v4, an advanced AI model, in aligning with the European Thyroid Association (ETA) - 2023 guidelines.

METHODS

The study utilized a structured questionnaire comprising 100 questions, divided into true/false and multiple-choice formats, reflecting real-world clinical scenarios in thyroid nodule management. These questions encompassed diagnostic criteria, treatment options, follow-up protocols, and patient counseling. ChatGPT response was evaluated for accuracy, consistency, and comprehensiveness using a six-point Likert scale. The assessment occurred initially and was repeated after 14 days.

RESULTS

In the binary queries, the AI model showed an ability to correct some initially incorrect responses. However, there was a noticeable regression in certain responses. 8 of the 11 previously non-compliant responses remained unchanged, while 3 non-compliant responses were rectified. Conversely, 6 initially compliant answers transitioned to non-compliance after 14 days. In multiple-choice queries, the AI's performance was more consistent. A majority of the responses, 43 (86% of the total), were initially correct and maintained their correctness upon re-assessment. However, 4 responses that were initially incorrect remained unchanged, and 3 correct responses shifted to non-compliance over time.

CONCLUSION

ChatGPT exhibited improving potential as a clinical support tool in thyroid nodule management altgouh it showed varied performance for binary and multiple-choice questions.

CLINICAL TRIAL REGISTRATION

N/A.

Computerized diagnostic decision support systems - a comparative performance study of Isabel Pro vs. ChatGPT4

OBJECTIVES

Validate the diagnostic accuracy of the Artificial Intelligence Large Language Model ChatGPT4 by comparing diagnosis lists produced by ChatGPT4 to Isabel Pro.

METHODS

This study used 201 cases, comparing ChatGPT4 to Isabel Pro. Systems inputs were identical. Mean Reciprocal Rank (MRR) compares the correct diagnosis's rank between systems. Isabel Pro ranks by the frequency with which the symptoms appear in the reference dataset. The mechanism ChatGPT4 uses to rank the diagnoses is unknown. A Wilcoxon Signed Rank Sum test failed to reject the null hypothesis.

RESULTS

Both systems produced comprehensive differential diagnosis lists. Isabel Pro's list appears immediately upon submission, while ChatGPT4 takes several minutes. Isabel Pro produced 175 (87.1 %) correct diagnoses and ChatGPT4 165 (82.1 %). The MRR for ChatGPT4 was 0.428 (rank 2.31), and Isabel Pro was 0.389 (rank 2.57), an average rank of three for each. ChatGPT4 outperformed on Recall at Rank 1, 5, and 10, with Isabel Pro outperforming at 20, 30, and 40. The Wilcoxon Signed Rank Sum Test confirmed that the sample size was inadequate to conclude that the systems are equivalent. ChatGPT4 fabricated citations and DOIs, producing 145 correct references (87.9 %) but only 52 correct DOIs (31.5 %).

CONCLUSIONS

This study validates the promise of Clinical Diagnostic Decision Support Systems, including the Large Language Model form of artificial intelligence (AI). Until the issue of hallucination of references and, perhaps diagnoses, is resolved in favor of absolute accuracy, clinicians will make cautious use of Large Language Model systems in diagnosis, if at all.

Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records

BACKGROUND

Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to adverse health outcomes. The ability to efficiently extract key clinical information from a large volume of data in electronic health records (EHR) can improve understanding about the extent of the problem and developing effective interventions. This research aimed to test the efficacy of zero-shot prompt engineering applied to generative artificial intelligence (AI) models on their own and in combination with retrieval augmented generation (RAG), for the automating tasks of summarizing both structured and unstructured data in EHR and extracting important malnutrition information.

METHODOLOGY

We utilized Llama 2 13B model with zero-shot prompting. The dataset comprises unstructured and structured EHRs related to malnutrition management in 40 Australian RACFs. We employed zero-shot learning to the model alone first, then combined it with RAG to accomplish two tasks: generate structured summaries about the nutritional status of a client and extract key information about malnutrition risk factors. We utilized 25 notes in the first task and 1,399 in the second task. We evaluated the model's output of each task manually against a gold standard dataset.

RESULT

The evaluation outcomes indicated that zero-shot learning applied to generative AI model is highly effective in summarizing and extracting information about nutritional status of RACFs' clients. The generated summaries provided concise and accurate representation of the original data with an overall accuracy of 93.25%. The addition of RAG improved the summarization process, leading to a 6% increase and achieving an accuracy of 99.25%. The model also proved its capability in extracting risk factors with an accuracy of 90%. However, adding RAG did not further improve accuracy in this task. Overall, the model has shown a robust performance when information was explicitly stated in the notes; however, it could encounter hallucination limitations, particularly when details were not explicitly provided.

CONCLUSION

This study demonstrates the high performance and limitations of applying zero-shot learning to generative AI models to automatic generation of structured summarization of EHRs data and extracting key clinical information. The inclusion of the RAG approach improved the model performance and mitigated the hallucination problem.

Examining the Potential of ChatGPT on Biomedical Information Retrieval: Fact-Checking Drug-Disease Associations

Large language models (LLMs) such as ChatGPT have recently attracted significant attention due to their impressive performance on many real-world tasks. These models have also demonstrated the potential in facilitating various biomedical tasks. However, little is known of their potential in biomedical information retrieval, especially identifying drug-disease associations. This study aims to explore the potential of ChatGPT, a popular LLM, in discerning drug-disease associations. We collected 2694 true drug-disease associations and 5662 false drug-disease pairs. Our approach involved creating various prompts to instruct ChatGPT in identifying these associations. Under varying prompt designs, ChatGPT's capability to identify drug-disease associations with an accuracy of 74.6-83.5% and 96.2-97.6% for the true and false pairs, respectively. This study shows that ChatGPT has the potential in identifying drug-disease associations and may serve as a helpful tool in searching pharmacy-related information. However, the accuracy of its insights warrants comprehensive examination before its implementation in medical practice.

Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey

PURPOSE OF REVIEW

This review highlights the timely relevance of artificial intelligence in enhancing assisted reproductive technologies (ARTs), particularly in-vitro fertilization (IVF). It underscores artificial intelligence's potential in revolutionizing patient outcomes and operational efficiency by addressing challenges in fertility diagnoses and procedures.

RECENT FINDINGS

Recent advancements in artificial intelligence, including machine learning and predictive modeling, are making significant strides in optimizing IVF processes such as medication dosing, scheduling, and embryological assessments. Innovations include artificial intelligence augmented diagnostic testing, predictive modeling for treatment outcomes, scheduling optimization, dosing and protocol selection, follicular and hormone monitoring, trigger timing, and improved embryo selection. These developments promise to refine treatment approaches, enhance patient engagement, and increase the accuracy and scalability of fertility treatments.

SUMMARY

The integration of artificial intelligence into reproductive medicine offers profound implications for clinical practice and research. By facilitating personalized treatment plans, standardizing procedures, and improving the efficiency of fertility clinics, artificial intelligence technologies pave the way for value-based, accessible, and efficient fertility services. Despite the promise, the full potential of artificial intelligence in ART will require ongoing validation and ethical considerations to ensure equitable and effective implementation.

Reliability of artificial intelligence chatbot responses to frequently asked questions in breast surgical oncology

INTRODUCTION

Artificial intelligence (AI)-driven chatbots, capable of simulating human-like conversations, are becoming more prevalent in healthcare. While this technology offers potential benefits in patient engagement and information accessibility, it raises concerns about potential misuse, misinformation, inaccuracies, and ethical challenges.

METHODS

This study evaluated a publicly available AI chatbot, ChatGPT, in its responses to nine questions related to breast cancer surgery selected from the American Society of Breast Surgeons' frequently asked questions (FAQ) patient education website. Four breast surgical oncologists assessed the responses for accuracy and reliability using a five-point Likert scale and the Patient Education Materials Assessment (PEMAT) Tool.

RESULTS

The average reliability score for ChatGPT in answering breast cancer surgery questions was 3.98 out of 5.00. Surgeons unanimously found the responses understandable and actionable per the PEMAT criteria. The consensus found ChatGPT's overall performance was appropriate, with minor or no inaccuracies.

CONCLUSION

ChatGPT demonstrates good reliability in responding to breast cancer surgery queries, with minor, nonharmful inaccuracies. Its answers are accurate, clear, and easy to comprehend. Notably, ChatGPT acknowledged its informational role and did not attempt to replace medical advice or discourage users from seeking input from a healthcare professional.

Recent progress in artificial intelligence and machine learning for novel diabetes mellitus medications development

Diabetes mellitus, stemming from either insulin resistance or inadequate insulin secretion, represents a complex ailment that results in prolonged hyperglycemia and severe complications. Patients endure severe ramifications such as kidney disease, vision impairment, cardiovascular disorders, and susceptibility to infections, leading to significant physical suffering and imposing substantial socio-economic burdens. This condition has evolved into an increasingly severe health crisis. There is an urgent need to develop new treatments with improved efficacy and fewer adverse effects to meet clinical demands. However, novel drug development is costly, time-consuming and often associated with side effects and suboptimal efficacy, making it a major challenge. Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized drug development across its comprehensive lifecycle, spanning drug discovery, preclinical studies, clinical trials, and post-market surveillance. These technologies have significantly accelerated the identification of promising therapeutic candidates, optimized trial designs, and enhanced post-approval safety monitoring. Recent advances in AI, including data augmentation, interpretable AI, and integration of AI with traditional experimental methods, offer promising strategies for overcoming the challenges inherent in AI-based drug discovery. Despite these advancements, there exists a notable gap in comprehensive reviews detailing AI and ML applications throughout the entirety of developing medications for diabetes mellitus. This review aims to fill this gap by evaluating the impact and potential of AI and ML technologies at various stages of diabetes mellitus drug development. By synthesizing current research findings and technological advances so as to effectively control diabetes mellitus and mitigate its far-reaching social and economic impacts. The integration of AI and ML promises to revolutionize diabetes mellitus treatment strategies, offering hope for improved patient outcomes and reduced healthcare burdens worldwide.

ChatFFA: An ophthalmic chat system for unified vision-language understanding and question answering for fundus fluorescein angiography

Existing automatic analysis of fundus fluorescein angiography (FFA) images faces limitations, including a predetermined set of possible image classifications and being confined to text-based question-answering (QA) approaches. This study aims to address these limitations by developing an end-to-end unified model that utilizes synthetic data to train a visual question-answering model for FFA images. To achieve this, we employed ChatGPT to generate 4,110,581 QA pairs for a large FFA dataset, which encompassed a total of 654,343 FFA images from 9,392 participants. We then fine-tuned the Bootstrapping Language-Image Pre-training (BLIP) framework to enable simultaneous handling of vision and language. The performance of the fine-tuned model (ChatFFA) was thoroughly evaluated through automated and manual assessments, as well as case studies based on an external validation set, demonstrating satisfactory results. In conclusion, our ChatFFA system paves the way for improved efficiency and feasibility in medical imaging analysis by leveraging generative large language models.

Evaluating Large Language Models for Automated Reporting and Data Systems Categorization: Cross-Sectional Study

BACKGROUND

Large language models show promise for improving radiology workflows, but their performance on structured radiological tasks such as Reporting and Data Systems (RADS) categorization remains unexplored.

OBJECTIVE

This study aims to evaluate 3 large language model chatbots-Claude-2, GPT-3.5, and GPT-4-on assigning RADS categories to radiology reports and assess the impact of different prompting strategies.

METHODS

This cross-sectional study compared 3 chatbots using 30 radiology reports (10 per RADS criteria), using a 3-level prompting strategy: zero-shot, few-shot, and guideline PDF-informed prompts. The cases were grounded in Liver Imaging Reporting & Data System (LI-RADS) version 2018, Lung CT (computed tomography) Screening Reporting & Data System (Lung-RADS) version 2022, and Ovarian-Adnexal Reporting & Data System (O-RADS) magnetic resonance imaging, meticulously prepared by board-certified radiologists. Each report underwent 6 assessments. Two blinded reviewers assessed the chatbots' response at patient-level RADS categorization and overall ratings. The agreement across repetitions was assessed using Fleiss κ.

RESULTS

Claude-2 achieved the highest accuracy in overall ratings with few-shot prompts and guideline PDFs (prompt-2), attaining 57% (17/30) average accuracy over 6 runs and 50% (15/30) accuracy with k-pass voting. Without prompt engineering, all chatbots performed poorly. The introduction of a structured exemplar prompt (prompt-1) increased the accuracy of overall ratings for all chatbots. Providing prompt-2 further improved Claude-2's performance, an enhancement not replicated by GPT-4. The interrun agreement was substantial for Claude-2 (k=0.66 for overall rating and k=0.69 for RADS categorization), fair for GPT-4 (k=0.39 for both), and fair for GPT-3.5 (k=0.21 for overall rating and k=0.39 for RADS categorization). All chatbots showed significantly higher accuracy with LI-RADS version 2018 than with Lung-RADS version 2022 and O-RADS (P<.05); with prompt-2, Claude-2 achieved the highest overall rating accuracy of 75% (45/60) in LI-RADS version 2018.

CONCLUSIONS

When equipped with structured prompts and guideline PDFs, Claude-2 demonstrated potential in assigning RADS categories to radiology cases according to established criteria such as LI-RADS version 2018. However, the current generation of chatbots lags in accurately categorizing cases based on more recent RADS criteria.

Cutting Edge to Cutting Time: Can ChatGPT Improve the Radiologist's Reporting?

Radiology-structured reports (SR) have many advantages over free text (FT), but the wide implementation of SR is still lagging. A powerful tool such as GPT-4 can address this issue. We aim to employ a web-based reporting tool powered by GPT-4 capable of converting FT to SR and then evaluate its impact on reporting time and report quality. Thirty abdominopelvic CT scans were reported by two radiologists across two sessions (15 scans each): a control session using traditional reporting methods and an AI-assisted session employing a GPT-4-powered web application to structure free text into structured reports. For each radiologist, the output included 15 control finalized reports, 15 AI-assisted pre-edits, and 15 post-edit finalized reports. Reporting turnaround times were assessed, including total reporting time (TRT) and case reporting time (TATc). Quality assessments were conducted by two blinded radiologists. TRT and TATc have decreased with the use of the AI-assisted reporting tool, although statistically not significant (p-value > 0.05). Mean TATc for RAD-1 decreased from 00:20:08 to 00:16:30 (hours:minutes:seconds) and TRT decreased from 05:02:00 to 04:08:00. Mean TATc for RAD-2 decreased from 00:12:04 to 00:10:04 and TRT decreased from 03:01:00 to 02:31:00. Quality scores of the finalized reports with and without AI-assistance were comparable with no significant differences. Adjusting the AI-assisted TATc by removing the editing time showed statistically significant results compared to the control for both radiologists (p-value < 0.05). The AI-assisted reporting tool can generate SR while reducing TRT and TATc without sacrificing report quality. Editing time is a potential area for further improvement.

The potential, limitations, and future of diagnostics enhanced by generative artificial intelligence

OBJECTIVES

This short communication explores the potential, limitations, and future directions of generative artificial intelligence (GAI) in enhancing diagnostics.

METHODS

This commentary reviews current applications and advancements in GAI, particularly focusing on its integration into medical diagnostics. It examines the role of GAI in supporting medical interviews, assisting in differential diagnosis, and aiding clinical reasoning through the lens of dual-process theory. The discussion is supported by recent examples and theoretical frameworks to illustrate the practical and potential uses of GAI in medicine.

RESULTS

GAI shows significant promise in enhancing diagnostic processes by supporting the translation of patient descriptions into visual formats, providing differential diagnoses, and facilitating complex clinical reasoning. However, limitations such as the potential for generating medical misinformation, known as hallucinations, exist. Furthermore, the commentary highlights the integration of GAI with both intuitive and analytical decision-making processes in clinical diagnostics, demonstrating potential improvements in both the speed and accuracy of diagnoses.

CONCLUSIONS

While GAI presents transformative potential for medical diagnostics, it also introduces risks that must be carefully managed. Future advancements should focus on refining GAI technologies to better align with human diagnostic reasoning, ensuring GAI enhances rather than replaces the medical professionals' expertise.

Utilizing ChatGPT for Curriculum Learning in Developing a Clinical Grade Pneumothorax Detection Model: A Multisite Validation Study

Background: Pneumothorax detection is often challenging, particularly when radiographic features are subtle. This study introduces a deep learning model that integrates curriculum learning and ChatGPT to enhance the detection of pneumothorax in chest X-rays. Methods: The model training began with large, easily detectable pneumothoraces, gradually incorporating smaller, more complex cases to prevent performance plateauing. The training dataset comprised 6445 anonymized radiographs, validated across multiple sites, and further tested for generalizability in diverse clinical subgroups. Performance metrics were analyzed using descriptive statistics. Results: The model achieved a sensitivity of 0.97 and a specificity of 0.97, with an area under the curve (AUC) of 0.98, demonstrating a performance comparable to that of many FDA-approved devices. Conclusions: This study suggests that a structured approach to training deep learning models, through curriculum learning and enhanced data extraction via natural language processing, can facilitate and improve the training of AI models for pneumothorax detection.

Impact Analysis of COVID-19 Pandemic on Hospital Reviews on Dianping Website in Shanghai, China: Empirical Study

BACKGROUND

In the era of the internet, individuals have increasingly accustomed themselves to gathering necessary information and expressing their opinions on public web-based platforms. The health care sector is no exception, as these comments, to a certain extent, influence people's health care decisions. During the onset of the COVID-19 pandemic, how the medical experience of Chinese patients and their evaluations of hospitals have changed remains to be studied. Therefore, we plan to collect patient medical visit data from the internet to reflect the current status of medical relationships under specific circumstances.

OBJECTIVE

This study aims to explore the differences in patient comments across various stages (during, before, and after) of the COVID-19 pandemic, as well as among different types of hospitals (children's hospitals, maternity hospitals, and tumor hospitals). Additionally, by leveraging ChatGPT (OpenAI), the study categorizes the elements of negative hospital evaluations. An analysis is conducted on the acquired data, and potential solutions that could improve patient satisfaction are proposed. This study is intended to assist hospital managers in providing a better experience for patients who are seeking care amid an emergent public health crisis.

METHODS

Selecting the top 50 comprehensive hospitals nationwide and the top specialized hospitals (children's hospitals, tumor hospitals, and maternity hospitals), we collected patient reviews from these hospitals on the Dianping website. Using ChatGPT, we classified the content of negative reviews. Additionally, we conducted statistical analysis using SPSS (IBM Corp) to examine the scoring and composition of negative evaluations.

RESULTS

A total of 30,317 pieces of effective comment information were collected from January 1, 2018, to August 15, 2023, including 7696 pieces of negative comment information. Manual inspection results indicated that ChatGPT had an accuracy rate of 92.05%. The F1-score was 0.914. The analysis of this data revealed a significant correlation between the comments and ratings received by hospitals during the pandemic. Overall, there was a significant increase in average comment scores during the outbreak (P<.001). Furthermore, there were notable differences in the composition of negative comments among different types of hospitals (P<.001). Children's hospitals received sensitive feedback regarding waiting times and treatment effectiveness, while patients at maternity hospitals showed a greater concern for the attitude of health care providers. Patients at tumor hospitals expressed a desire for timely examinations and treatments, especially during the pandemic period.

CONCLUSIONS

The COVID-19 pandemic had some association with patient comment scores. There were variations in the scores and content of comments among different types of specialized hospitals. Using ChatGPT to analyze patient comment content represents an innovative approach for statistically assessing factors contributing to patient dissatisfaction. The findings of this study could provide valuable insights for hospital administrators to foster more harmonious physician-patient relationships and enhance hospital performance during public health emergencies.

Evaluating the Quality of Postpartum Hemorrhage Nursing Care Plans Generated by Artificial Intelligence Models

BACKGROUND

With the rapidly advancing technological landscape of health care, evaluating the potential use of artificial intelligence (AI) models to prepare nursing care plans is of great importance.

PURPOSE

The purpose of this study was to evaluate the quality of nursing care plans created by AI for the management of postpartum hemorrhage (PPH).

METHODS

This cross-sectional exploratory study involved creating a scenario for an imaginary patient with PPH. Information was put into 3 AI platforms (GPT-4, LaMDA, Med-PaLM) on consecutive days without prior conversation. Care plans were evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale.

RESULTS

Med-PaLM exhibited superior quality in developing the care plan compared with LaMDA ( Z = 4.354; P = .000) and GPT-4 ( Z = 3.126; P = .029).

CONCLUSIONS

Our findings suggest that despite the strong performance of Med-PaLM, AI, in its current state, is unsuitable for use with real patients.

An evaluation of the capabilities of language models and nurses in providing neonatal clinical decision support

AIM

To assess the clinical reasoning capabilities of two large language models, ChatGPT-4 and Claude-2.0, compared to those of neonatal nurses during neonatal care scenarios.

DESIGN

A cross-sectional study with a comparative evaluation using a survey instrument that included six neonatal intensive care unit clinical scenarios.

PARTICIPANTS

32 neonatal intensive care nurses with 5-10 years of experience working in the neonatal intensive care units of three medical centers.

METHODS

Participants responded to 6 written clinical scenarios. Simultaneously, we asked ChatGPT-4 and Claude-2.0 to provide initial assessments and treatment recommendations for the same scenarios. The responses from ChatGPT-4 and Claude-2.0 were then scored by certified neonatal nurse practitioners for accuracy, completeness, and response time.

RESULTS

Both models demonstrated capabilities in clinical reasoning for neonatal care, with Claude-2.0 significantly outperforming ChatGPT-4 in clinical accuracy and speed. However, limitations were identified across the cases in diagnostic precision, treatment specificity, and response lag.

CONCLUSIONS

While showing promise, current limitations reinforce the need for deep refinement before ChatGPT-4 and Claude-2.0 can be considered for integration into clinical practice. Additional validation of these tools is important to safely leverage this Artificial Intelligence technology for enhancing clinical decision-making.

IMPACT

The study provides an understanding of the reasoning accuracy of new Artificial Intelligence models in neonatal clinical care. The current accuracy gaps of ChatGPT-4 and Claude-2.0 need to be addressed prior to clinical usage.

Assessing Public Knowledge and Acceptance of Using Artificial Intelligence Doctors as a Partial Alternative to Human Doctors in Saudi Arabia: A Cross-Sectional Study

Objective To assess the public acceptance of using artificial intelligence (AI) doctors to diagnose and treat patients as a partial alternative to human physicians in Saudi Arabia. Methodology An observational cross-sectional study was conducted from January to March 2024. A link to an online questionnaire was distributed through social media applications to citizens and residents aged 18 years and older across various regions in Saudi Arabia. The sample size was calculated using the Raosoft online survey size calculator, which estimated that the minimum sample size should be 385. Results Of the 386 participants surveyed, 85.8% reported being aware of AI, and 47.9% reported having some knowledge about different AI fields in daily life. However, almost one-third (32.9%) reported a lack of knowledge about the use of AI in healthcare. In terms of acceptance, 52.3% of respondents indicated they felt comfortable with the use of AI tools as partial alternatives to human doctors, and 30.8% believed AI is useful in the field of health. The most common concern (63.7%) about the use of AI tools accessible to patients was the difficulty of describing symptoms using these tools. Conclusion The findings of this study provide valuable insights into the public's knowledge and acceptance of AI in medicine within the Saudi Arabian context. Overall, this study underscores the importance of proactively addressing the public's concerns and knowledge gaps regarding AI in healthcare. By fostering greater understanding and acceptance, healthcare stakeholders can better harness the potential of AI to improve patient outcomes and enhance the efficiency of medical services in Saudi Arabia.

Research on entity relation extraction for Chinese medical text

Currently, the primary challenges in entity relation extraction are the existence of overlapping relations and cascading errors. In addressing these issues, both CasRel and TPLinker have demonstrated their competitiveness. This study aims to explore the application of these two models in the context of entity relation extraction from Chinese medical text. We evaluate the performance of these models using the publicly available dataset CMeIE and further enhance their capabilities through the incorporation of pre-trained models that are tailored to the specific characteristics of the text. The experimental findings demonstrate that the TPLinker model exhibits a heightened and consistent boosting effect compared to CasRel, while also attaining superior performance through the utilization of advanced pre-trained models. Notably, the MacBERT + TPLinker combination emerges as the optimal choice, surpassing the benchmark model by 12.45% and outperforming the leading model ERNIE-Health 3.0 in the CBLUE challenge by 2.31%.

Generative artificial intelligence in healthcare from the perspective of digital media: Applications, opportunities and challenges

Introduction

The emergence and application of generative artificial intelligence/large language models (hereafter GenAI LLMs) have the potential for significant impact on the healthcare industry. However, there is currently a lack of systematic research on GenAI LLMs in healthcare based on reliable data. This article aims to conduct an exploratory study of the application of GenAI LLMs (i.e., ChatGPT) in healthcare from the perspective of digital media (i.e., online news), including the application scenarios, potential opportunities, and challenges.

Methods

This research used thematic qualitative text analysis in five steps: firstly, developing main topical categories based on relevant articles; secondly, encoding the search keywords using these categories; thirdly, conducting searches for news articles via Google ; fourthly, encoding the sub-categories using the elaborate category system; and finally, conducting category-based analysis and presenting the results. Natural language processing techniques, including the TermRaider and AntConc tool, were applied in the aforementioned steps to assist in text qualitative analysis. Additionally, this study built a framework, using for analyzing the above three topics, from the perspective of five different stakeholders, including healthcare demanders and providers.

Results

This study summarizes 26 applications (e.g., provide medical advice, provide diagnosis and triage recommendations, provide mental health support, etc.), 21 opportunities (e.g., make healthcare more accessible, reduce healthcare costs, improve patients care, etc.), and 17 challenges (e.g., generate inaccurate/misleading/wrong answers, raise privacy concerns, lack of transparency, etc.), and analyzes the reasons for the formation of these key items and the links between the three research topics.

Conclusions

The application of GenAI LLMs in healthcare is primarily focused on transforming the way healthcare demanders access medical services (i.e., making it more intelligent, refined, and humane) and optimizing the processes through which healthcare providers offer medical services (i.e., simplifying, ensuring timeliness, and reducing errors). As the application becomes more widespread and deepens, GenAI LLMs is expected to have a revolutionary impact on traditional healthcare service models, but it also inevitably raises ethical and security concerns. Furthermore, GenAI LLMs applied in healthcare is still in the initial stage, which can be accelerated from a specific healthcare field (e.g., mental health) or a specific mechanism (e.g., GenAI LLMs' economic benefits allocation mechanism applied to healthcare) with empirical or clinical research.

Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans

The integration of teledermatology and artificial intelligence (AI) marks a significant advancement in dermatologic care. This study examines the synergistic interplay between these two domains, highlighting their collective impact on enhancing the accuracy, accessibility, and efficiency of teledermatologic services. Teledermatology expands dermatologic care to remote and underserved areas, and AI technologies show considerable potential in analyzing dermatologic images and performing various tasks involved in teledermatology consultations. Such integration facilitates rapid, precise diagnoses, personalized treatment plans, and data-driven insights. Our explorative study involved designing a GPT-based chatbot named "Dr. DermBot" and exploring its performance in a teledermatologic consultation process. The design phase focused on the chatbot's ability to conduct consultations autonomously. The subsequent testing phase assessed its performance against the backdrop of current teledermatologic practices, exploring the potential of AI and chatbots to simulate and potentially enhance teledermatologic health care. Our study demonstrates the promising future of combining teledermatology with AI. It also brings to light ethical and legal concerns, including the protection of patient data privacy and adherence to regulatory standards. The union of teledermatology and AI not only aims to enhance the precision of teledermatologic diagnoses but also broadens the accessibility of dermatologic services to previously underserved populations, benefiting patients, health care providers, and the overall health care system.

Evaluating ChatGPT-4's Accuracy in Identifying Final Diagnoses Within Differential Diagnoses Compared With Those of Physicians: Experimental Study for Diagnostic Cases

BACKGROUND

The potential of artificial intelligence (AI) chatbots, particularly ChatGPT with GPT-4 (OpenAI), in assisting with medical diagnosis is an emerging research area. However, it is not yet clear how well AI chatbots can evaluate whether the final diagnosis is included in differential diagnosis lists.

OBJECTIVE

This study aims to assess the capability of GPT-4 in identifying the final diagnosis from differential-diagnosis lists and to compare its performance with that of physicians for case report series.

METHODS

We used a database of differential-diagnosis lists from case reports in the American Journal of Case Reports, corresponding to final diagnoses. These lists were generated by 3 AI systems: GPT-4, Google Bard (currently Google Gemini), and Large Language Models by Meta AI 2 (LLaMA2). The primary outcome was focused on whether GPT-4's evaluations identified the final diagnosis within these lists. None of these AIs received additional medical training or reinforcement. For comparison, 2 independent physicians also evaluated the lists, with any inconsistencies resolved by another physician.

RESULTS

The 3 AIs generated a total of 1176 differential diagnosis lists from 392 case descriptions. GPT-4's evaluations concurred with those of the physicians in 966 out of 1176 lists (82.1%). The Cohen κ coefficient was 0.63 (95% CI 0.56-0.69), indicating a fair to good agreement between GPT-4 and the physicians' evaluations.

CONCLUSIONS

GPT-4 demonstrated a fair to good agreement in identifying the final diagnosis from differential-diagnosis lists, comparable to physicians for case report series. Its ability to compare differential diagnosis lists with final diagnoses suggests its potential to aid clinical decision-making support through diagnostic feedback. While GPT-4 showed a fair to good agreement for evaluation, its application in real-world scenarios and further validation in diverse clinical environments are essential to fully understand its utility in the diagnostic process.

Can ChatGPT provide high-quality patient information on male lower urinary tract symptoms suggestive of benign prostate enlargement?

BACKGROUND

ChatGPT has recently emerged as a novel resource for patients' disease-specific inquiries. There is, however, limited evidence assessing the quality of the information. We evaluated the accuracy and quality of the ChatGPT's responses on male lower urinary tract symptoms (LUTS) suggestive of benign prostate enlargement (BPE) when compared to two reference resources.

METHODS

Using patient information websites from the European Association of Urology and the American Urological Association as reference material, we formulated 88 BPE-centric questions for ChatGPT 4.0+. Independently and in duplicate, we compared the ChatGPT's responses and the reference material, calculating accuracy through F1 score, precision, and recall metrics. We used a 5-point Likert scale for quality rating. We evaluated examiner agreement using the interclass correlation coefficient and assessed the difference in the quality scores with the Wilcoxon signed-rank test.

RESULTS

ChatGPT addressed all (88/88) LUTS/BPE-related questions. For the 88 questions, the recorded F1 score was 0.79 (range: 0-1), precision 0.66 (range: 0-1), recall 0.97 (range: 0-1), and the quality score had a median of 4 (range = 1-5). Examiners had a good level of agreement (ICC = 0.86). We found no statistically significant difference between the scores given by the examiners and the overall quality of the responses (p = 0.72).

DISCUSSION

ChatGPT demostrated a potential utility in educating patients about BPE/LUTS, its prognosis, and treatment that helps in the decision-making process. One must exercise prudence when recommending this as the sole information outlet. Additional studies are needed to completely understand the full extent of AI's efficacy in delivering patient education in urology.

Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis

BACKGROUND

Burnout among health care professionals is a significant concern, with detrimental effects on health care service quality and patient outcomes. The use of the electronic health record (EHR) system has been identified as a significant contributor to burnout among health care professionals.

OBJECTIVE

This systematic review and meta-analysis aims to assess the prevalence of burnout among health care professionals associated with the use of the EHR system, thereby providing evidence to improve health information systems and develop strategies to measure and mitigate burnout.

METHODS

We conducted a comprehensive search of the PubMed, Embase, and Web of Science databases for English-language peer-reviewed articles published between January 1, 2009, and December 31, 2022. Two independent reviewers applied inclusion and exclusion criteria, and study quality was assessed using the Joanna Briggs Institute checklist and the Newcastle-Ottawa Scale. Meta-analyses were performed using R (version 4.1.3; R Foundation for Statistical Computing), with EndNote X7 (Clarivate) for reference management.

RESULTS

The review included 32 cross-sectional studies and 5 case-control studies with a total of 66,556 participants, mainly physicians and registered nurses. The pooled prevalence of burnout among health care professionals in cross-sectional studies was 40.4% (95% CI 37.5%-43.2%). Case-control studies indicated a higher likelihood of burnout among health care professionals who spent more time on EHR-related tasks outside work (odds ratio 2.43, 95% CI 2.31-2.57).

CONCLUSIONS

The findings highlight the association between the increased use of the EHR system and burnout among health care professionals. Potential solutions include optimizing EHR systems, implementing automated dictation or note-taking, employing scribes to reduce documentation burden, and leveraging artificial intelligence to enhance EHR system efficiency and reduce the risk of burnout.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42021281173; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021281173.

Implications of Large Language Models for Quality and Efficiency of Neurologic Care: Emerging Issues in Neurology

Large language models (LLMs) are advanced artificial intelligence (AI) systems that excel in recognizing and generating human-like language, possibly serving as valuable tools for neurology-related information tasks. Although LLMs have shown remarkable potential in various areas, their performance in the dynamic environment of daily clinical practice remains uncertain. This article outlines multiple limitations and challenges of using LLMs in clinical settings that need to be addressed, including limited clinical reasoning, variable reliability and accuracy, reproducibility bias, self-serving bias, sponsorship bias, and potential for exacerbating health care disparities. These challenges are further compounded by practical business considerations and infrastructure requirements, including associated costs. To overcome these hurdles and harness the potential of LLMs effectively, this article includes considerations for health care organizations, researchers, and neurologists contemplating the use of LLMs in clinical practice. It is essential for health care organizations to cultivate a culture that welcomes AI solutions and aligns them seamlessly with health care operations. Clear objectives and business plans should guide the selection of AI solutions, ensuring they meet organizational needs and budget considerations. Engaging both clinical and nonclinical stakeholders can help secure necessary resources, foster trust, and ensure the long-term sustainability of AI implementations. Testing, validation, training, and ongoing monitoring are pivotal for successful integration. For neurologists, safeguarding patient data privacy is paramount. Seeking guidance from institutional information technology resources for informed, compliant decisions, and remaining vigilant against biases in LLM outputs are essential practices in responsible and unbiased utilization of AI tools. In research, obtaining institutional review board approval is crucial when dealing with patient data, even if deidentified, to ensure ethical use. Compliance with established guidelines like SPIRIT-AI, MI-CLAIM, and CONSORT-AI is necessary to maintain consistency and mitigate biases in AI research. In summary, the integration of LLMs into clinical neurology offers immense promise while presenting formidable challenges. Awareness of these considerations is vital for harnessing the potential of AI in neurologic care effectively and enhancing patient care quality and safety. The article serves as a guide for health care organizations, researchers, and neurologists navigating this transformative landscape.

Performance evaluation of ChatGPT in detecting diagnostic errors and their contributing factors: an analysis of 545 case reports of diagnostic errors

BACKGROUND

Manual chart review using validated assessment tools is a standardised methodology for detecting diagnostic errors. However, this requires considerable human resources and time. ChatGPT, a recently developed artificial intelligence chatbot based on a large language model, can effectively classify text based on suitable prompts. Therefore, ChatGPT can assist manual chart reviews in detecting diagnostic errors.

OBJECTIVE

This study aimed to clarify whether ChatGPT could correctly detect diagnostic errors and possible factors contributing to them based on case presentations.

METHODS

We analysed 545 published case reports that included diagnostic errors. We imputed the texts of case presentations and the final diagnoses with some original prompts into ChatGPT (GPT-4) to generate responses, including the judgement of diagnostic errors and contributing factors of diagnostic errors. Factors contributing to diagnostic errors were coded according to the following three taxonomies: Diagnosis Error Evaluation and Research (DEER), Reliable Diagnosis Challenges (RDC) and Generic Diagnostic Pitfalls (GDP). The responses on the contributing factors from ChatGPT were compared with those from physicians.

RESULTS

ChatGPT correctly detected diagnostic errors in 519/545 cases (95%) and coded statistically larger numbers of factors contributing to diagnostic errors per case than physicians: DEER (median 5 vs 1, p<0.001), RDC (median 4 vs 2, p<0.001) and GDP (median 4 vs 1, p<0.001). The most important contributing factors of diagnostic errors coded by ChatGPT were 'failure/delay in considering the diagnosis' (315, 57.8%) in DEER, 'atypical presentation' (365, 67.0%) in RDC, and 'atypical presentation' (264, 48.4%) in GDP.

CONCLUSION

ChatGPT accurately detects diagnostic errors from case presentations. ChatGPT may be more sensitive than manual reviewing in detecting factors contributing to diagnostic errors, especially for 'atypical presentation'.

Comparison of ChatGPT version 3.5 & 4 for utility in respiratory medicine education using clinical case scenarios

Integration of ChatGPT in Respiratory medicine presents a promising avenue for enhancing clinical practice and pedagogical approaches. This study compares the performance of ChatGPT version 3.5 and 4 in respiratory medicine, emphasizing its potential in clinical decision support and medical education using clinical cases. Results indicate moderate performance highlighting limitations in handling complex case scenarios. Compared to ChatGPT 3.5, version 4 showed greater promise as a pedagogical tool, providing interactive learning experiences. While serving as a preliminary decision support tool clinically, caution is advised, stressing the need for ongoing validation. Future research should refine its clinical capabilities for optimal integration into medical education and practice.

ChatGPT vs. web search for patient questions: what does ChatGPT do better?

PURPOSE

Chat generative pretrained transformer (ChatGPT) has the potential to significantly impact how patients acquire medical information online. Here, we characterize the readability and appropriateness of ChatGPT responses to a range of patient questions compared to results from traditional web searches.

METHODS

Patient questions related to the published Clinical Practice Guidelines by the American Academy of Otolaryngology-Head and Neck Surgery were sourced from existing online posts. Questions were categorized using a modified Rothwell classification system into (1) fact, (2) policy, and (3) diagnosis and recommendations. These were queried using ChatGPT and traditional web search. All results were evaluated on readability (Flesch Reading Ease and Flesch-Kinkaid Grade Level) and understandability (Patient Education Materials Assessment Tool). Accuracy was assessed by two blinded clinical evaluators using a three-point ordinal scale.

RESULTS

54 questions were organized into fact (37.0%), policy (37.0%), and diagnosis (25.8%). The average readability for ChatGPT responses was lower than traditional web search (FRE: 42.3 ± 13.1 vs. 55.6 ± 10.5, p < 0.001), while the PEMAT understandability was equivalent (93.8% vs. 93.5%, p = 0.17). ChatGPT scored higher than web search for questions the 'Diagnosis' category (p < 0.01); there was no difference in questions categorized as 'Fact' (p = 0.15) or 'Policy' (p = 0.22). Additional prompting improved ChatGPT response readability (FRE 55.6 ± 13.6, p < 0.01).

CONCLUSIONS

ChatGPT outperforms web search in answering patient questions related to symptom-based diagnoses and is equivalent in providing medical facts and established policy. Appropriate prompting can further improve readability while maintaining accuracy. Further patient education is needed to relay the benefits and limitations of this technology as a source of medial information.

An artificial intelligence language model improves readability of burns first aid information

AIMS

This study aimed to assess the potential of using an artificial intelligence (AI) large language model to improve the readability of burns first aid information.

METHODS

An AI language model (ChatGPT-3) was used to rewrite content from the top 50 English-language webpages containing burns first aid information to be understandable by an individual with the literacy level of an 11-year-old, as recommended by the American Medical Association and Health Education England. The assessment of readability was conducted using five validated tools.

RESULTS

In their original form, only 4% of the patient education materials (PEMs) met the target readability level across all tools. The median grade was 6.9 (SD=1.1). One-sample one-tailed t-test revealed that this was not significantly below the target (p = .31). After AI-modification, 18% of PEMs reached the target level using all tools, with a median grade of 6 (SD=0.9), which was significantly below the target level (p < .001). Once rewritten using AI, paired t-test demonstrated that all readability scores improved significantly (p < .001).

CONCLUSION

Utilising an AI language model proved an effective and viable method for enhancing readability of burns first aid information.

Harnessing ChatGPT for Thematic Analysis: Are We Ready?

ChatGPT (OpenAI) is an advanced natural language processing tool with growing applications across various disciplines in medical research. Thematic analysis, a qualitative research method to identify and interpret patterns in data, is one application that stands to benefit from this technology. This viewpoint explores the use of ChatGPT in three core phases of thematic analysis within a medical context: (1) direct coding of transcripts, (2) generating themes from a predefined list of codes, and (3) preprocessing quotes for manuscript inclusion. Additionally, we explore the potential of ChatGPT to generate interview transcripts, which may be used for training purposes. We assess the strengths and limitations of using ChatGPT in these roles, highlighting areas where human intervention remains necessary. Overall, we argue that ChatGPT can function as a valuable tool during analysis, enhancing the efficiency of the thematic analysis and offering additional insights into the qualitative data. While ChatGPT may not adequately capture the full context of each participant, it can serve as an additional member of the analysis team, contributing to researcher triangulation through knowledge building and sensemaking.

Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support

OBJECTIVES

To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts.

MATERIALS AND METHODS

We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4. We surveyed 5 CDS experts to rate the human-generated and AI-generated summaries on a scale from 1 (strongly disagree) to 5 (strongly agree) for the 4 metrics: clarity, completeness, accuracy, and usefulness.

RESULTS

Five CDS experts participated in the survey. A total of 16 human-generated summaries and 8 AI-generated summaries were assessed. Among the top 8 rated summaries, five were generated by GPT-4. AI-generated summaries demonstrated high levels of clarity, accuracy, and usefulness, similar to the human-generated summaries. Moreover, AI-generated summaries exhibited significantly higher completeness and usefulness compared to the human-generated summaries (AI: 3.4 ± 1.2, human: 2.7 ± 1.2, P = .001).

CONCLUSION

End-user comments provide clinicians' immediate feedback to CDS alerts and can serve as a direct and valuable data resource for improving CDS delivery. Traditionally, these comments may not be considered in the CDS review process due to their unstructured nature, large volume, and the presence of redundant or irrelevant content. Our study demonstrates that GPT-4 is capable of distilling these comments into summaries characterized by high clarity, accuracy, and completeness. AI-generated summaries are equivalent and potentially better than human-generated summaries. These AI-generated summaries could provide CDS experts with a novel means of reviewing user comments to rapidly optimize CDS alerts both online and offline.

Leveraging large language models for generating responses to patient messages-a subjective analysis

OBJECTIVE

This study aimed to develop and assess the performance of fine-tuned large language models for generating responses to patient messages sent via an electronic health record patient portal.

MATERIALS AND METHODS

Utilizing a dataset of messages and responses extracted from the patient portal at a large academic medical center, we developed a model (CLAIR-Short) based on a pre-trained large language model (LLaMA-65B). In addition, we used the OpenAI API to update physician responses from an open-source dataset into a format with informative paragraphs that offered patient education while emphasizing empathy and professionalism. By combining with this dataset, we further fine-tuned our model (CLAIR-Long). To evaluate fine-tuned models, we used 10 representative patient portal questions in primary care to generate responses. We asked primary care physicians to review generated responses from our models and ChatGPT and rated them for empathy, responsiveness, accuracy, and usefulness.

RESULTS

The dataset consisted of 499 794 pairs of patient messages and corresponding responses from the patient portal, with 5000 patient messages and ChatGPT-updated responses from an online platform. Four primary care physicians participated in the survey. CLAIR-Short exhibited the ability to generate concise responses similar to provider's responses. CLAIR-Long responses provided increased patient educational content compared to CLAIR-Short and were rated similarly to ChatGPT's responses, receiving positive evaluations for responsiveness, empathy, and accuracy, while receiving a neutral rating for usefulness.

CONCLUSION

This subjective analysis suggests that leveraging large language models to generate responses to patient messages demonstrates significant potential in facilitating communication between patients and healthcare providers.

A cross-sectional comparative study: ChatGPT 3.5 versus diverse levels of medical experts in the diagnosis of ENT diseases

PURPOSE

With recent advances in artificial intelligence (AI), it has become crucial to thoroughly evaluate its applicability in healthcare. This study aimed to assess the accuracy of ChatGPT in diagnosing ear, nose, and throat (ENT) pathology, and comparing its performance to that of medical experts.

METHODS

We conducted a cross-sectional comparative study where 32 ENT cases were presented to ChatGPT 3.5, ENT physicians, ENT residents, family medicine (FM) specialists, second-year medical students (Med2), and third-year medical students (Med3). Each participant provided three differential diagnoses. The study analyzed diagnostic accuracy rates and inter-rater agreement within and between participant groups and ChatGPT.

RESULTS

The accuracy rate of ChatGPT was 70.8%, being not significantly different from ENT physicians or ENT residents. However, a significant difference in correctness rate existed between ChatGPT and FM specialists (49.8%, p < 0.001), and between ChatGPT and medical students (Med2 47.5%, p < 0.001; Med3 47%, p < 0.001). Inter-rater agreement for the differential diagnosis between ChatGPT and each participant group was either poor or fair. In 68.75% of cases, ChatGPT failed to mention the most critical diagnosis.

CONCLUSIONS

ChatGPT demonstrated accuracy comparable to that of ENT physicians and ENT residents in diagnosing ENT pathology, outperforming FM specialists, Med2 and Med3. However, it showed limitations in identifying the most critical diagnosis.

Transforming Virtual Healthcare: The Potentials of ChatGPT-4omni in Telemedicine

The introduction of OpenAI's ChatGPT-4omni (GPT-4o) represents a potential advancement in virtual healthcare and telemedicine. GPT-4o excels in processing audio, visual, and textual data in real time, offering possible enhancements in understanding natural language in both English and non-English contexts. Furthermore, the new "Temporary Chat" feature may improve privacy and data confidentiality during interactions, potentially increasing integration with healthcare systems. These innovations promise to enhance communication clarity, facilitate the integration of medical images, and increase data privacy in online consultations. This editorial explores some future implications of these advancements for telemedicine, highlighting the necessity for further research on reliability and the integration of advanced language models with human expertise.

Enhancing Musculoskeletal Injection Safety: Evaluating Checklists Generated by Artificial Intelligence and Revising the Preformed Checklist

Background Musculoskeletal disorders are a significant global health issue, necessitating advanced management strategies such as intra-articular and extra-articular injections to alleviate pain, inflammation, and mobility challenges. As the adoption of these interventions by physicians grows, the importance of robust safety protocols becomes paramount. This study evaluates the effectiveness of conversational artificial intelligence (AI), particularly versions 3.5 and 4 of Chat Generative Pre-trained Transformer (ChatGPT), in creating patient safety checklists for managing musculoskeletal injections to enhance the preparation of safety documentation. Methodology A quantitative analysis was conducted to evaluate AI-generated safety checklists against a preformed checklist adapted from reputable medical sources. Adherence of the generated checklists to the preformed checklist was calculated and classified. The Wilcoxon signed-rank test was used to assess the performance differences between ChatGPT versions 3.5 and 4. Results ChatGPT-4 showed superior adherence to the preformed checklist compared to ChatGPT-3.5, with both versions classified as very good in safety protocol creation. Although no significant differences were present in the sign-in and sign-out parts of the checklists of both versions, ChatGPT-4 had significantly higher scores in the procedure planning part (p = 0.007), and its overall performance was also higher (p < 0.001). Subsequently, the preformed checklist was revised to incorporate new contributions from ChatGPT. Conclusions ChatGPT, especially version 4, proved effective in generating patient safety checklists for musculoskeletal injections, highlighting the potential of AI to streamline clinical practices. Further enhancements are necessary to fully meet the medical standards.

ChatGPT and Clinical Decision Support: Scope, Application, and Limitations

This study examines ChatGPT's role in clinical decision support, by analyzing its scope, application, and limitations. By analyzing patient data and providing evidence-based recommendations, ChatGPT, an AI language model, can help healthcare professionals make well-informed decisions. This study examines ChatGPT's use in clinical decision support, including diagnosis and treatment planning. However, it acknowledges limitations like biases, lack of contextual understanding, and human oversight and also proposes a framework for the future clinical decision support system. Understanding these factors will allow healthcare professionals to utilize ChatGPT effectively and make accurate clinical decisions. Further research is needed to understand the implications of using ChatGPT in healthcare settings and to develop safeguards for responsible use.

Does small talk with a medical provider affect ChatGPT's medical counsel? Performance of ChatGPT on USMLE with and without distractions

Efforts are being made to improve the time effectiveness of healthcare providers. Artificial intelligence tools can help transcript and summarize physician-patient encounters and produce medical notes and medical recommendations. However, in addition to medical information, discussion between healthcare and patients includes small talk and other information irrelevant to medical concerns. As Large Language Models (LLMs) are predictive models building their response based on the words in the prompts, there is a risk that small talk and irrelevant information may alter the response and the suggestion given. Therefore, this study aims to investigate the impact of medical data mixed with small talk on the accuracy of medical advice provided by ChatGPT. USMLE step 3 questions were used as a model for relevant medical data. We use both multiple-choice and open-ended questions. First, we gathered small talk sentences from human participants using the Mechanical Turk platform. Second, both sets of USLME questions were arranged in a pattern where each sentence from the original questions was followed by a small talk sentence. ChatGPT 3.5 and 4 were asked to answer both sets of questions with and without the small talk sentences. Finally, a board-certified physician analyzed the answers by ChatGPT and compared them to the formal correct answer. The analysis results demonstrate that the ability of ChatGPT-3.5 to answer correctly was impaired when small talk was added to medical data (66.8% vs. 56.6%; p = 0.025). Specifically, for multiple-choice questions (72.1% vs. 68.9%; p = 0.67) and for the open questions (61.5% vs. 44.3%; p = 0.01), respectively. In contrast, small talk phrases did not impair ChatGPT-4 ability in both types of questions (83.6% and 66.2%, respectively). According to these results, ChatGPT-4 seems more accurate than the earlier 3.5 version, and it appears that small talk does not impair its capability to provide medical recommendations. Our results are an important first step in understanding the potential and limitations of utilizing ChatGPT and other LLMs for physician-patient interactions, which include casual conversations.

A Systematic Review of ChatGPT and Other Conversational Large Language Models in Healthcare

Background

The launch of the Chat Generative Pre-trained Transformer (ChatGPT) in November 2022 has attracted public attention and academic interest to large language models (LLMs), facilitating the emergence of many other innovative LLMs. These LLMs have been applied in various fields, including healthcare. Numerous studies have since been conducted regarding how to employ state-of-the-art LLMs in health-related scenarios to assist patients, doctors, and public health administrators.

Objective

This review aims to summarize the applications and concerns of applying conversational LLMs in healthcare and provide an agenda for future research on LLMs in healthcare.

Methods

We utilized PubMed, ACM, and IEEE digital libraries as primary sources for this review. We followed the guidance of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRIMSA) to screen and select peer-reviewed research articles that (1) were related to both healthcare applications and conversational LLMs and (2) were published before September 1st, 2023, the date when we started paper collection and screening. We investigated these papers and classified them according to their applications and concerns.

Results

Our search initially identified 820 papers according to targeted keywords, out of which 65 papers met our criteria and were included in the review. The most popular conversational LLM was ChatGPT from OpenAI (60), followed by Bard from Google (1), Large Language Model Meta AI (LLaMA) from Meta (1), and other LLMs (5). These papers were classified into four categories in terms of their applications: 1) summarization, 2) medical knowledge inquiry, 3) prediction, and 4) administration, and four categories of concerns: 1) reliability, 2) bias, 3) privacy, and 4) public acceptability. There are 49 (75%) research papers using LLMs for summarization and/or medical knowledge inquiry, and 58 (89%) research papers expressing concerns about reliability and/or bias. We found that conversational LLMs exhibit promising results in summarization and providing medical knowledge to patients with a relatively high accuracy. However, conversational LLMs like ChatGPT are not able to provide reliable answers to complex health-related tasks that require specialized domain expertise. Additionally, no experiments in our reviewed papers have been conducted to thoughtfully examine how conversational LLMs lead to bias or privacy issues in healthcare research.

Conclusions

Future studies should focus on improving the reliability of LLM applications in complex health-related tasks, as well as investigating the mechanisms of how LLM applications brought bias and privacy issues. Considering the vast accessibility of LLMs, legal, social, and technical efforts are all needed to address concerns about LLMs to promote, improve, and regularize the application of LLMs in healthcare.

Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability Framework for Safe and Effective Large Language Models in Medical Education: Narrative Review and Qualitative Study

BACKGROUND

The world has witnessed increased adoption of large language models (LLMs) in the last year. Although the products developed using LLMs have the potential to solve accessibility and efficiency problems in health care, there is a lack of available guidelines for developing LLMs for health care, especially for medical education.

OBJECTIVE

The aim of this study was to identify and prioritize the enablers for developing successful LLMs for medical education. We further evaluated the relationships among these identified enablers.

METHODS

A narrative review of the extant literature was first performed to identify the key enablers for LLM development. We additionally gathered the opinions of LLM users to determine the relative importance of these enablers using an analytical hierarchy process (AHP), which is a multicriteria decision-making method. Further, total interpretive structural modeling (TISM) was used to analyze the perspectives of product developers and ascertain the relationships and hierarchy among these enablers. Finally, the cross-impact matrix-based multiplication applied to a classification (MICMAC) approach was used to determine the relative driving and dependence powers of these enablers. A nonprobabilistic purposive sampling approach was used for recruitment of focus groups.

RESULTS

The AHP demonstrated that the most important enabler for LLMs was credibility, with a priority weight of 0.37, followed by accountability (0.27642) and fairness (0.10572). In contrast, usability, with a priority weight of 0.04, showed negligible importance. The results of TISM concurred with the findings of the AHP. The only striking difference between expert perspectives and user preference evaluation was that the product developers indicated that cost has the least importance as a potential enabler. The MICMAC analysis suggested that cost has a strong influence on other enablers. The inputs of the focus group were found to be reliable, with a consistency ratio less than 0.1 (0.084).

CONCLUSIONS

This study is the first to identify, prioritize, and analyze the relationships of enablers of effective LLMs for medical education. Based on the results of this study, we developed a comprehendible prescriptive framework, named CUC-FATE (Cost, Usability, Credibility, Fairness, Accountability, Transparency, and Explainability), for evaluating the enablers of LLMs in medical education. The study findings are useful for health care professionals, health technology experts, medical technology regulators, and policy makers.

Geriatrics and artificial intelligence in Spain (Ger-IA project): talking to ChatGPT, a nationwide survey

PURPOSE

The purposes of the study was to describe the degree of agreement between geriatricians with the answers given by an AI tool (ChatGPT) in response to questions related to different areas in geriatrics, to study the differences between specialists and residents in geriatrics in terms of the degree of agreement with ChatGPT, and to analyse the mean scores obtained by areas of knowledge/domains.

METHODS

An observational study was conducted involving 126 doctors from 41 geriatric medicine departments in Spain. Ten questions about geriatric medicine were posed to ChatGPT, and doctors evaluated the AI's answers using a Likert scale. Sociodemographic variables were included. Questions were categorized into five knowledge domains, and means and standard deviations were calculated for each.

RESULTS

130 doctors answered the questionnaire. 126 doctors (69.8% women, mean age 41.4 [9.8]) were included in the final analysis. The mean score obtained by ChatGPT was 3.1/5 [0.67]. Specialists rated ChatGPT lower than residents (3.0/5 vs. 3.3/5 points, respectively, P < 0.05). By domains, ChatGPT ​​scored better (M: 3.96; SD: 0.71) in general/theoretical questions rather than in complex decisions/end-of-life situations (M: 2.50; SD: 0.76) and answers related to diagnosis/performing of complementary tests obtained the lowest ones (M: 2.48; SD: 0.77).

CONCLUSION

Scores presented big variability depending on the area of knowledge. Questions related to theoretical aspects of challenges/future in geriatrics obtained better scores. When it comes to complex decision-making, appropriateness of the therapeutic efforts or decisions about diagnostic tests, professionals indicated a poorer performance. AI is likely to be incorporated into some areas of medicine, but it would still present important limitations, mainly in complex medical decision-making.