Can Large Language Models Safely Address Patient Questions Following Cataract Surgery? (2023)

Abstract: Recent advances in large language models (LLMs) have generated significant interest in their application across various domains including healthcare. However, there is limited data on their safety and performance in real-world scenarios. This study uses data collected using an autonomous telemedicine clinical assistant. The assistant asks symptom-based questions to elicit patient concerns and allows patients to ask questions about their post-operative recovery. We utilise real-world postoperative questions posed to the assistant by a cohort of 120 patients to examine the safety and appropriateness of responses generated by a recent popular LLM by OpenAI, ChatGPT. We demonstrate that LLMs have the potential to helpfully address routine patient queries following routine surgery. However, important limitations around the safety of today’s models exist which must be considered.

## introduction in recent years, large language models have gained immense popularity. these models are capable of generating and understanding natural language at previously unimaginable levels, making them indispensable in a wide-range of natural language applications. in the last few months, this popularity has been fuelled by the recent breakthrough of ope-nai's chatgpt, which has made llms accessible to the wider public. llms are versatile and can be repurposed to work in a variety of different domains. developers and researchers around the world have demonstrated the usefulness of these transformer-based models in sectors like retail @xcite , finance @xcite and software engineering (surameery and shakor, 2023) but one sector that still hasn't absorbed the benefits of large language models is healthcare. most * these authors contributed equally to this work healthcare interactions are conversations in natural language @xcite , which means llms have huge potential in this area, but the complexities around safety and reliability of these models raise concerns that have yet to be addressed @xcite . there have been attempts to address this problem by approaches like fine-tuning, prompt-engineering, prompt-tuning @xcite , rlhf @xcite , but the lack of benchmarks and consensus around objective evaluation metrics for this domain makes this a challenging problem to solve. authors of med-palm @xcite ) have attempted to address this issue by releasing benchmarks and strategies that can be used to evaluate the usefulness of these models in the healthcare setting. in this work, we adapt these evaluation strategies to test how a large language model responds to patient questions following cataract surgery. this is a significant clinical use case as approximately 20m cataract surgeries are performed each year in the world @xcite . we use the data collected by an autonomous telemedicine clinical assistant that elicits post-operative concerns from patients by asking them symptom-based questions about their operated eye. we use the questions asked by patients to this assistant to examine the safety and appropriateness of responses from openai's chatgpt. ## related work there has been significant interest in either developing medical large language models @xcite @xcite or using existing large language models like gpt-4 for healthcare applications @xcite . however, many authors have pointed out the current shortcomings of llms for healthcare @xcite and ethical barriers to their adoption @xcite . within healthcare, many authors have demonstrated the performance of various llms in tasks with clearly defined 'correct' answers, such as its performance on physician licensing examinations like the united states medical licensing examination (usmle) or speciality-specific exams like the ophthalmic knowledge assessment program (okap) (singhal et al., 2022; nori et al., 2023; teebagy et al., 2023; gilson et al., 2023; antaki et al., 2023). whilst impressive in its demonstration of clinical 'knowledge' through its performance in multiplechoice examinations, for the majority of real-world clinical tasks such as note-taking and medical conversations, evaluation of what constitutes 'good' for performance has been challenging @xcite . indeed, the authors of the landmark holistic evaluation of language models (helm) framework @xcite highlighted the importance of benchmarking against human-evaluation metrics to identify issues like hallucinations or disinformation. correspondingly, previous authors have utilised various human evaluation metrics for healthcaredomain llm tasks. in a study by @xcite , lay people assessed chatgpt's medical question answers firstly for whether the answers were distinguishable from a human, and secondly via a likert scale for their trust in the use of chatbot responses. alternatively, other authors have used specialist graders to assess the suitability of answers. @xcite presented a simplified approach us-ing only two questions with binary outcomes for "precision" and "suitability" as assessed by five retinal specialists in response to a set of hypothetical frequently asked questions in the context of a retina clinic. just just just one question i do have a slight shadow in my left eye on the left hand side thank you for your question. it is not uncommon for patients to experience some changes in their vision after cataract surgery, and this can include the appearance of shadows or halos around lights. it is important to keep in mind that it can take some time for your vision to fully adjust and for any post-surgical effects to resolve. in the meantime, it is important to follow your doctor's instructions for care and use of any prescribed eye drops. if you are experiencing any significant discomfort or if the shadow in your vision does not improve over time, it is important to contact your doctor for further evaluation i have double vision. double vision, or diplopia, is a common side effect after cataract surgery. it usually goes away on its own within a few days to a week. in the meantime, you can try using a single vision eyeglass or patch over one eye to help reduce the double vision. it's important to continue following your post-operative instructions and taking your prescribed eye drops. if the double vision persists or worsens, it's important to let your ophthalmologist know so they can assess the cause and determine the appropriate treatment. in the meantime, try to avoid activities that require precise vision, such as driving, until the double vision improves. ## conclusion even with no fine-tuning and minimal prompt engineering, we demonstrate that llms like chat-gpt have the potential to helpfully address routine patient queries from a real-world dataset of transcribed questions following cataract surgery. however, it is crucial to acknowledge the potential constraints associated with the safety of these models when deployed for healthcare applications.