Are AI language models such as ChatGPT ready to improve the care of individuals with epilepsy?

Artificial intelligence: Can it help us better grasp the idea of epilepsy? An exploratory dialogue with ChatGPT and DALL·E 2

BACKGROUND

The conceptual definition of epilepsy has been changing over decades and remains debatable. We assessed how artificial intelligence (AI) conceives epilepsy and its impact on a person's life through verbal and visual material.

METHODS

We asked the Chat Generative Pre-Trained Transformer (ChatGPT, OpenAI) to define epilepsy and its impact. Prompts from ChatGPT were transferred to another AI tool DALL·E 2 (Open AI) to generate visual images based on verbal input.

RESULTS

The ChatGPT definition on epilepsy relied on both its conceptual and practical definitions. It titled epilepsy to be "a neurological disorder characterized by recurring seizures" that has significant impact on patients' lives and is diagnosed after two or more unprovoked seizures or if there is a high risk of future seizures. ChatGPT presented nine issues - seizure-related injuries, limitations on daily activities, emotional and psychological impact, social stigma and isolation, educational and employment challenges, relationship and family dynamics, medication side effects, financial burden, and coexisting conditions - as major consequences of epilepsy. AI-generated images ranged from direct portrayals of these phenomena to abstract imagery but were mostly deprived of symbolic elements and visual metaphors.

CONCLUSION

We showed that AI can identify and visually interpret the burden of epilepsy from medical, societal and economical perspectives. However, the imagery created is not figurative and does not follow allegorical narratives put forward by epilepsy specialists in similar studies. The ability of AI models to lead an in-depth discussion on epilepsy remains questionable and should be explored with more advanced AI tools.

The promise of AI Large Language Models for Epilepsy care

Artificial intelligence (AI) has been supporting our digital life for decades, but public interest in this has exploded with the recognition of large language models, such as GPT-4. We examine and evaluate the potential uses for generative AI technologies in epilepsy and neurological services. Generative AI could not only improve patient care and safety by refining communication and removing certain barriers to healthcare but may also extend to streamlining a doctor's practice through strategies such as automating paperwork. Challenges with the integration of generative AI in epilepsy services are also explored and include the risk of producing inaccurate and biased information. The impact generative AI could have on the provision of healthcare, both positive and negative, should be understood and considered carefully when deciding on the steps that need to be taken before AI is ready for use in hospitals and epilepsy services.

Artificial intelligence performance in clinical neurology queries: the ChatGPT model

INTRODUCTION

The use of artificial intelligence technology is progressively expanding and advancing in the health and biomedical literature. Since its launch, ChatGPT has rapidly gained popularity and become one of the fastest-growing artificial intelligence applications in history. This study evaluated the accuracy and comprehensiveness of ChatGPT-generated responses to medical queries in clinical neurology.

METHODS

We directed 216 questions from different subspecialties to ChatGPT. The questions were classified into three categories: multiple-choice, descriptive, and binary (yes/no answers). Each question in all categories was subjectively rated as easy, medium, or hard according to its difficulty level. Questions that also tested for intuitive clinical thinking and reasoning ability were evaluated in a separate category.

RESULTS

ChatGPT correctly answered 141 questions (65.3%). No significant difference was detected in the accuracy and comprehensiveness scale scores or correct answer rates in comparisons made according to the question style or difficulty level. However, a comparative analysis assessing question characteristics revealed significantly lower accuracy and comprehensiveness scale scores and correct answer rates for questions based on interpretations that required critical thinking (p = 0.007, 0.007, and 0.001, respectively).

CONCLUSION

ChatGPT had a moderate overall performance in clinical neurology and demonstrated inadequate performance in answering questions that required interpretation and critical thinking. It also displayed limited performance in specific subspecialties. It is essential to acknowledge the limitations of artificial intelligence and diligently verify medical information produced by such models using reliable sources.

Potential merits and flaws of large language models in epilepsy care: A critical review

The current pace of development and applications of large language models (LLMs) is unprecedented and will impact future medical care significantly. In this critical review, we provide the background to better understand these novel artificial intelligence (AI) models and how LLMs can be of future use in the daily care of people with epilepsy. Considering the importance of clinical history taking in diagnosing and monitoring epilepsy-combined with the established use of electronic health records-a great potential exists to integrate LLMs in epilepsy care. We present the current available LLM studies in epilepsy. Furthermore, we highlight and compare the most commonly used LLMs and elaborate on how these models can be applied in epilepsy. We further discuss important drawbacks and risks of LLMs, and we provide recommendations for overcoming these limitations.

Innovations in Medicine: Exploring ChatGPT's Impact on Rare Disorder Management

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field's potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.

Large Language Models and Artificial Intelligence: A Primer for Plastic Surgeons on the Demonstrated and Potential Applications, Promises, and Limitations of ChatGPT

BACKGROUND

The rapidly evolving field of artificial intelligence (AI) holds great potential for plastic surgeons. ChatGPT, a recently released AI large language model (LLM), promises applications across many disciplines, including healthcare.

OBJECTIVES

The aim of this article was to provide a primer for plastic surgeons on AI, LLM, and ChatGPT, including an analysis of current demonstrated and proposed clinical applications.

METHODS

A systematic review was performed identifying medical and surgical literature on ChatGPT's proposed clinical applications. Variables assessed included applications investigated, command tasks provided, user input information, AI-emulated human skills, output validation, and reported limitations.

RESULTS

The analysis included 175 articles reporting on 13 plastic surgery applications and 116 additional clinical applications, categorized by field and purpose. Thirty-four applications within plastic surgery are thus proposed, with relevance to different target audiences, including attending plastic surgeons (n = 17, 50%), trainees/educators (n = 8, 24.0%), researchers/scholars (n = 7, 21%), and patients (n = 2, 6%). The 15 identified limitations of ChatGPT were categorized by training data, algorithm, and ethical considerations.

CONCLUSIONS

Widespread use of ChatGPT in plastic surgery will depend on rigorous research of proposed applications to validate performance and address limitations. This systemic review aims to guide research, development, and regulation to safely adopt AI in plastic surgery.

Potential and Limitations of ChatGPT 3.5 and 4.0 as a Source of COVID-19 Information: Comprehensive Comparative Analysis of Generative and Authoritative Information

BACKGROUND

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has necessitated reliable and authoritative information for public guidance. The World Health Organization (WHO) has been a primary source of such information, disseminating it through a question and answer format on its official website. Concurrently, ChatGPT 3.5 and 4.0, a deep learning-based natural language generation system, has shown potential in generating diverse text types based on user input.

OBJECTIVE

This study evaluates the accuracy of COVID-19 information generated by ChatGPT 3.5 and 4.0, assessing its potential as a supplementary public information source during the pandemic.

METHODS

We extracted 487 COVID-19-related questions from the WHO's official website and used ChatGPT 3.5 and 4.0 to generate corresponding answers. These generated answers were then compared against the official WHO responses for evaluation. Two clinical experts scored the generated answers on a scale of 0-5 across 4 dimensions-accuracy, comprehensiveness, relevance, and clarity-with higher scores indicating better performance in each dimension. The WHO responses served as the reference for this assessment. Additionally, we used the BERT (Bidirectional Encoder Representations from Transformers) model to generate similarity scores (0-1) between the generated and official answers, providing a dual validation mechanism.

RESULTS

The mean (SD) scores for ChatGPT 3.5-generated answers were 3.47 (0.725) for accuracy, 3.89 (0.719) for comprehensiveness, 4.09 (0.787) for relevance, and 3.49 (0.809) for clarity. For ChatGPT 4.0, the mean (SD) scores were 4.15 (0.780), 4.47 (0.641), 4.56 (0.600), and 4.09 (0.698), respectively. All differences were statistically significant (P<.001), with ChatGPT 4.0 outperforming ChatGPT 3.5. The BERT model verification showed mean (SD) similarity scores of 0.83 (0.07) for ChatGPT 3.5 and 0.85 (0.07) for ChatGPT 4.0 compared with the official WHO answers.

CONCLUSIONS

ChatGPT 3.5 and 4.0 can generate accurate and relevant COVID-19 information to a certain extent. However, compared with official WHO responses, gaps and deficiencies exist. Thus, users of ChatGPT 3.5 and 4.0 should also reference other reliable information sources to mitigate potential misinformation risks. Notably, ChatGPT 4.0 outperformed ChatGPT 3.5 across all evaluated dimensions, a finding corroborated by BERT model validation.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.

Leveraging ChatGPT in the Pediatric Neurology Clinic: Practical Considerations for Use to Improve Efficiency and Outcomes

BACKGROUND

Artificial intelligence (AI) is progressively influencing healthcare sectors, including pediatric neurology. This paper aims to investigate the potential and limitations of using ChatGPT, a large language model (LLM) developed by OpenAI, in an outpatient pediatric neurology clinic. The analysis focuses on the tool's capabilities in enhancing clinical efficiency, productivity, and patient education.

METHOD

This is an opinion-based exploration supplemented with practical examples. We assessed ChatGPT's utility in administrative and educational tasks such as drafting medical necessity letters and creating patient educational materials.

RESULTS

ChatGPT showed efficacy in streamlining administrative work, particularly in drafting administrative letters and formulating personalized patient education materials. However, the model has limitations in performing higher-order tasks like formulating nuanced differential diagnoses. Additionally, ethical and legal concerns, including data privacy and the potential dissemination of misinformation, warrant cautious implementation.

CONCLUSIONS

The integration of AI tools like ChatGPT in pediatric neurology clinics has demonstrated promising results in boosting efficiency and patient education, despite present limitations and ethical concerns. As technology advances, we anticipate future applications may extend to more complex clinical tasks like precise differential diagnoses and treatment strategy guidance. Careful, patient-centered implementation is essential for leveraging the potential benefits of AI in pediatric neurology effectively.

Exploring the role of ChatGPT in patient care (diagnosis and treatment) and medical research: A systematic review

Background

ChatGPT is an artificial intelligence based tool developed by OpenAI (California, USA). This systematic review examines the potential of ChatGPT in patient care and its role in medical research.

Methods

The systematic review was done according to the PRISMA guidelines. Embase, Scopus, PubMed and Google Scholar data bases were searched. We also searched preprint data bases. Our search was aimed to identify all kinds of publications, without any restrictions, on ChatGPT and its application in medical research, medical publishing and patient care. We used search term "ChatGPT". We reviewed all kinds of publications including original articles, reviews, editorial/ commentaries, and even letter to the editor. Each selected records were analysed using ChatGPT and responses generated were compiled in a table. The word table was transformed in to a PDF and was further analysed using ChatPDF.

Results

We reviewed full texts of 118 articles. ChatGPT can assist with patient enquiries, note writing, decision-making, trial enrolment, data management, decision support, research support, and patient education. But the solutions it offers are usually insufficient and contradictory, raising questions about their originality, privacy, correctness, bias, and legality. Due to its lack of human-like qualities, ChatGPT's legitimacy as an author is questioned when used for academic writing. ChatGPT generated contents have concerns with bias and possible plagiarism.

Conclusion

Although it can help with patient treatment and research, there are issues with accuracy, authorship, and bias. ChatGPT can serve as a "clinical assistant" and be a help in research and scholarly writing.

Universal skepticism of ChatGPT: a review of early literature on chat generative pre-trained transformer

ChatGPT, a new language model developed by OpenAI, has garnered significant attention in various fields since its release. This literature review provides an overview of early ChatGPT literature across multiple disciplines, exploring its applications, limitations, and ethical considerations. The review encompasses Scopus-indexed publications from November 2022 to April 2023 and includes 156 articles related to ChatGPT. The findings reveal a predominance of negative sentiment across disciplines, though subject-specific attitudes must be considered. The review highlights the implications of ChatGPT in many fields including healthcare, raising concerns about employment opportunities and ethical considerations. While ChatGPT holds promise for improved communication, further research is needed to address its capabilities and limitations. This literature review provides insights into early research on ChatGPT, informing future investigations and practical applications of chatbot technology, as well as development and usage of generative AI.

Ethical Considerations of Using ChatGPT in Health Care

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.

Performance of GPT-3.5 and GPT-4 on the Japanese Medical Licensing Examination: Comparison Study

BACKGROUND

The competence of ChatGPT (Chat Generative Pre-Trained Transformer) in non-English languages is not well studied.

OBJECTIVE

This study compared the performances of GPT-3.5 (Generative Pre-trained Transformer) and GPT-4 on the Japanese Medical Licensing Examination (JMLE) to evaluate the reliability of these models for clinical reasoning and medical knowledge in non-English languages.

METHODS

This study used the default mode of ChatGPT, which is based on GPT-3.5; the GPT-4 model of ChatGPT Plus; and the 117th JMLE in 2023. A total of 254 questions were included in the final analysis, which were categorized into 3 types, namely general, clinical, and clinical sentence questions.

RESULTS

The results indicated that GPT-4 outperformed GPT-3.5 in terms of accuracy, particularly for general, clinical, and clinical sentence questions. GPT-4 also performed better on difficult questions and specific disease questions. Furthermore, GPT-4 achieved the passing criteria for the JMLE, indicating its reliability for clinical reasoning and medical knowledge in non-English languages.

CONCLUSIONS

GPT-4 could become a valuable tool for medical education and clinical support in non-English-speaking regions, such as Japan.