Abstract The use of large language models (LLMs) in clinical medicine is currently thriving. Effectively transferring LLMs’ pertinent theoretical knowledge from computer science to their application in clinical medicine is crucial. Prompt engineering has shown potential as an effective method in this regard. To explore the application of prompt engineering in LLMs and to examine the reliability of LLMs, different styles of prompts were designed and used to ask different LLMs about their agreement with the American Academy of Orthopedic Surgeons (AAOS) osteoarthritis (OA) evidence-based guidelines. Each question was asked 5 times. We compared the consistency of the findings with guidelines across different evidence levels for different prompts and assessed the reliability of different prompts by asking the same question 5 times. gpt-4-Web with ROT prompting had the highest overall consistency (62.9%) and a significant performance for strong recommendations, with a total consistency of 77.5%. The reliability of the different LLMs for different prompts was not stable (Fleiss kappa ranged from −0.002 to 0.984). This study revealed that different prompts had variable effects across various models, and the gpt-4-Web with ROT prompt was the most consistent. An appropriate prompt could improve the accuracy of responses to professional medical questions. 

Prompt engineering in consistency and reliability with the evidence-based guideline for LLMs

ChatGPT is a large language model trained on increasingly large datasets by OpenAI to perform language-based tasks. It is capable of answering multiple-choice questions, such as those posed by the dermatology SCE examination. We asked two iterations of ChatGPT: ChatGPT-3.5 and ChatGPT-4 84 multiple-choice sample questions from the sample dermatology SCE question bank. ChatGPT-3.5 achieved an overall score of 63.1%, and ChatGPT-4 scored 90.5% (a significant improvement in performance (p<0.001)). The typical pass mark for the dermatology SCE is 70-72%. ChatGPT-4 is therefore capable of answering clinical questions and achieving a passing grade in these sample questions. There are many possible educational and clinical implications for increasingly advanced artificial intelligence (AI) and its use in medicine, including in the diagnosis of dermatological conditions. Such advances should be embraced provided that patient safety is a core tenet, and the limitations of AI in the nuances of complex clinical cases are recognised.

Performance of ChatGPT on dermatology Specialty Certificate Examination multiple choice questions.

Performance of artificial intelligence chatbots in sleep medicine certification board exams: ChatGPT versus Google Bard.

PURPOSE
With the introduction of ChatGPT, artificial intelligence (AI)-based large language models (LLMs) are rapidly becoming popular within the scientific community. They use natural language processing to generate human-like responses to queries. However, the application of LLMs and comparison of the abilities among different LLMs with their human counterparts in ophthalmic care remain under-reported.


RECENT FINDINGS
Hitherto, studies in eye care have demonstrated the utility of ChatGPT in generating patient information, clinical diagnosis and passing ophthalmology question-based examinations, among others. LLMs' performance (median accuracy, %) is influenced by factors such as the iteration, prompts utilised and the domain. Human expert (86%) demonstrated the highest proficiency in disease diagnosis, while ChatGPT-4 outperformed others in ophthalmology examinations (75.9%), symptom triaging (98%) and providing information and answering questions (84.6%). LLMs exhibited superior performance in general ophthalmology but reduced accuracy in ophthalmic subspecialties. Although AI-based LLMs like ChatGPT are deemed more efficient than their human counterparts, these AIs are constrained by their nonspecific and outdated training, no access to current knowledge, generation of plausible-sounding 'fake' responses or hallucinations, inability to process images, lack of critical literature analysis and ethical and copyright issues. A comprehensive evaluation of recently published studies is crucial to deepen understanding of LLMs and the potential of these AI-based LLMs.


SUMMARY
Ophthalmic care professionals should undertake a conservative approach when using AI, as human judgement remains essential for clinical decision-making and monitoring the accuracy of information. This review identified the ophthalmic applications and potential usages which need further exploration. With the advancement of LLMs, setting standards for benchmarking and promoting best practices is crucial. Potential clinical deployment requires the evaluation of these LLMs to move away from artificial settings, delve into clinical trials and determine their usefulness in the real world.

Utility of artificial intelligence-based large language models in ophthalmic care.

Abstract The GPT-4 large language model (LLM) and ChatGPT chatbot have emerged as accessible and capable tools for generating English-language text in a variety of formats. GPT-4 has previously performed well when applied to questions from multiple standardized examinations. However, further evaluation of trustworthiness and accuracy of GPT-4 responses across various knowledge domains is essential before its use as a reference resource. Here, we assess GPT-4 performance on nine graduate-level examinations in the biomedical sciences (seven blinded), finding that GPT-4 scores exceed the student average in seven of nine cases and exceed all student scores for four exams. GPT-4 performed very well on fill-in-the-blank, short-answer, and essay questions, and correctly answered several questions on figures sourced from published manuscripts. Conversely, GPT-4 performed poorly on questions with figures containing simulated data and those requiring a hand-drawn answer. Two GPT-4 answer-sets were flagged as plagiarism based on answer similarity and some model responses included detailed hallucinations. In addition to assessing GPT-4 performance, we discuss patterns and limitations in GPT-4 capabilities with the goal of informing design of future academic examinations in the chatbot era. 

The model student: GPT-4 performance on graduate biomedical science exams

BACKGROUND
Chat Generative Pre-trained Transformer (ChatGPT), a large language model by OpenAI, and Bard, Google's artificial intelligence (AI) chatbot, have been evaluated in various contexts. This study aims to assess these models' proficiency in the part 1 Fellowship of the Royal College of Ophthalmologists (FRCOphth) Multiple Choice Question (MCQ) examination, highlighting their potential in medical education.


METHODS
Both models were tested on a sample question bank for the part 1 FRCOphth MCQ exam. Their performances were compared with historical human performance on the exam, focusing on the ability to comprehend, retain and apply information related to ophthalmology. We also tested it on the book 'MCQs for FRCOpth part 1', and assessed its performance across subjects.


RESULTS
ChatGPT demonstrated a strong performance, surpassing historical human pass marks and examination performance, while Bard underperformed. The comparison indicates the potential of certain AI models to match, and even exceed, human standards in such tasks.


CONCLUSION
The results demonstrate the potential of AI models, such as ChatGPT, in processing and applying medical knowledge at a postgraduate level. However, performance varied among different models, highlighting the importance of appropriate AI selection. The study underlines the potential for AI applications in medical education and the necessity for further investigation into their strengths and limitations.

Performance of ChatGPT and Bard on the official part 1 FRCOphth practice questions.

This paper describes a project, Callguide, to investigate the hypertext system Guide as an authoring shell for computer assisted language learning programs. The paper begins by considering the advantages of computers in CALL. It goes on to discuss the interface requirements for a CALL package. The Callguide programs were written, under a hypertext system without audio-visual facilities, to improve the intensive reading skills of advanced students who have A-level or equivalent in English as a Foreign Language and who are additionally motivated by the need to study a technical discipline in the foreign language. Here the discipline is computing, but the techniques would apply equally well to other subjects. The project has largely concentrated on exercises to improve the student's lexical skills and ability to understand, in detail, a technical text. Lexical skills include exercises to recognise antonyms and synonyms, word formation to correspond with part of speech and understanding idiomatic expressions. Discourse skills include maintaining coherence throughout a passage by verb correspondence in tense, mode and aspect, identifying referents of pronouns and relative pronouns, and traditional comprehension exercises including completing Cloze texts and connectivity matrices. A brief user evaluation of the project is followed by a conclusion.

CALLGUIDE: Using Programmable Hypertext as a Shell for CALL Programs

Performance of ChatGPT on a primary FRCA multiple choice question bank.

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Performance of ChatGPT and Bard on the official part 1 FRCOphth practice questions.

CALLGUIDE: Using Programmable Hypertext as a Shell for CALL Programs

Performance of ChatGPT on a primary FRCA multiple choice question bank.