Large language models (LLMs) such as GPT-4 hold great promise as transformative tools in health care, ranging from automating administrative tasks to augmenting clinical decision making. However, these models also pose a danger of perpetuating biases and delivering incorrect medical diagnoses, which can have a direct, harmful impact on medical care. We aimed to assess whether GPT-4 encodes racial and gender biases that impact its use in health care.Using the Azure OpenAI application interface, this model evaluation study tested whether GPT-4 encodes racial and gender biases and examined the impact of such biases on four potential applications of LLMs in the clinical domain-namely, medical education, diagnostic reasoning, clinical plan generation, and subjective patient assessment. We conducted experiments with prompts designed to resemble typical use of GPT-4 within clinical and medical education applications. We used clinical vignettes from NEJM Healer and from published research on implicit bias in health care. GPT-4 estimates of the demographic distribution of medical conditions were compared with true US prevalence estimates. Differential diagnosis and treatment planning were evaluated across demographic groups using standard statistical tests for significance between groups.We found that GPT-4 did not appropriately model the demographic diversity of medical conditions, consistently producing clinical vignettes that stereotype demographic presentations. The differential diagnoses created by GPT-4 for standardised clinical vignettes were more likely to include diagnoses that stereotype certain races, ethnicities, and genders. Assessment and plans created by the model showed significant association between demographic attributes and recommendations for more expensive procedures as well as differences in patient perception.Our findings highlight the urgent need for comprehensive and transparent bias assessments of LLM tools such as GPT-4 for intended use cases before they are integrated into clinical care. We discuss the potential sources of these biases and potential mitigation strategies before clinical implementation.Priscilla Chan and Mark Zuckerberg. 

Assessing the potential of GPT-4 to perpetuate racial and gender biases in health care: a model evaluation study.

Abstract The United States Medical Licensing Examination (USMLE) has been a subject of performance study for artificial intelligence (AI) models. However, their performance on questions involving USMLE soft skills remains unexplored. This study aimed to evaluate ChatGPT and GPT-4 on USMLE questions involving communication skills, ethics, empathy, and professionalism. We used 80 USMLE-style questions involving soft skills, taken from the USMLE website and the AMBOSS question bank. A follow-up query was used to assess the models’ consistency. The performance of the AI models was compared to that of previous AMBOSS users. GPT-4 outperformed ChatGPT, correctly answering 90% compared to ChatGPT’s 62.5%. GPT-4 showed more confidence, not revising any responses, while ChatGPT modified its original answers 82.5% of the time. The performance of GPT-4 was higher than that of AMBOSS's past users. Both AI models, notably GPT-4, showed capacity for empathy, indicating AI's potential to meet the complex interpersonal, ethical, and professional demands intrinsic to the practice of medicine. 

Comparing ChatGPT and GPT-4 performance in USMLE soft skill assessments

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles 

A whole-slide foundation model for digital pathology from real-world data.

There are enormous enthusiasm and concerns in applying large language models (LLMs) to healthcare. Yet current assumptions are based on general-purpose LLMs such as ChatGPT, which are not developed for medical use. This study develops a generative clinical LLM, GatorTronGPT, using 277 billion words of text including (1) 82 billion words of clinical text from 126 clinical departments and approximately 2 million patients at the University of Florida Health and (2) 195 billion words of diverse general English text. We train GatorTronGPT using a GPT-3 architecture with up to 20 billion parameters and evaluate its utility for biomedical natural language processing (NLP) and healthcare text generation. GatorTronGPT improves biomedical natural language processing. We apply GatorTronGPT to generate 20 billion words of synthetic text. Synthetic NLP models trained using synthetic text generated by GatorTronGPT outperform models trained using real-world clinical text. Physicians' Turing test using 1 (worst) to 9 (best) scale shows that there are no significant differences in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p < 0.001). This study provides insights into the opportunities and challenges of LLMs for medical research and healthcare. 

pdf/a-study-of-generative-large-language-model-for-medical-4a21u4puej.pdf

A study of generative large language model for medical research and healthcare

There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using 277 billion words of mixed clinical and English text with a GPT-3 architecture of 20 billion parameters. GatorTronGPT improves biomedical natural language processing for medical research. Synthetic NLP models trained using GatorTronGPT generated text outperform NLP models trained using real-world clinical text. Physicians Turing test using 1 (worst) to 9 (best) scale shows that there is no significant difference in linguistic readability (p = 0.22; 6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91; 7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p<0.001). This study provides insights on the opportunities and challenges of LLMs for medical research and healthcare.

pdf/a-study-of-generative-large-language-model-for-medical-ms0bf2kc.pdf

A Study of Generative Large Language Model for Medical Research and Healthcare

Physicians make critical time-constrained decisions every day. Clinical predictive models can help physicians and administrators make decisions by forecasting clinical and operational events. Existing structured data-based clinical predictive models have limited use in everyday practice owing to complexity in data processing, as well as model development and deployment1-3. Here we show that unstructured clinical notes from the electronic health record can enable the training of clinical language models, which can be used as all-purpose clinical predictive engines with low-resistance development and deployment. Our approach leverages recent advances in natural language processing4,5 to train a large language model for medical language (NYUTron) and subsequently fine-tune it across a wide range of clinical and operational predictive tasks. We evaluated our approach within our health system for five such tasks: 30-day all-cause readmission prediction, in-hospital mortality prediction, comorbidity index prediction, length of stay prediction, and insurance denial prediction. We show that NYUTron has an area under the curve (AUC) of 78.7-94.9%, with an improvement of 5.36-14.7% in the AUC compared with traditional models. We additionally demonstrate the benefits of pretraining with clinical text, the potential for increasing generalizability to different sites through fine-tuning and the full deployment of our system in a prospective, single-arm trial. These results show the potential for using clinical language models in medicine to read alongside physicians and provide guidance at the point of care. 

Health system-scale language models are all-purpose prediction engines


 
 Existing clinical prediction algorithms mostly leverage small cohorts of structured data (e.g., medical imaging or laboratory data). However, large language models have demonstrated the ability to utilize unstructured data to outperform other machine learning approaches given sufficient data. Training large language models on unstructured clinical notes offers a possible alternative to structured data algorithm development in clinical tasks.
 
 
 
 An unlabeled dataset of over seven million unstructured clinical notes (e.g., radiology reports and patient histories) was collected from four hospitals within the NYU Langone Health (NYULH) system and utilized to pre-train a bidirectional encoder representation with transformer (BERT) model. This model was further fine-tuned using a labeled dataset of discharge summaries to predict 30-day all-cause readmission. The resulting model, termed NYUTron, was assessed using a held-out retrospective cohort of patients from June to December 2021.
 
 
 
 Over the period of the retrospective study, there were a total 1,072 neurosurgery patients. The BERT model achieved an area under the receiver operating curve of 0.7883, a recall of 95.1% at a precision of 27.3%, and an accuracy of 82.1%.
 
 
 
 This study demonstrates how large language models and unstructured clinical notes can be used to provide information to physicians on clinical tasks with a flexible framework that is amenable to modification for other clinical tasks.


754 Prediction of 30-day All-Cause Readmission of Neurosurgery Patients Using Large Language Models

e18759 Background: Breast cancer patients at specialty cancer hospitals have a 10% lower risk of death in the first year vs those at community hospitals, where 77% of cancer patients are treated. However, there are barriers to seeking and following up care at the former. One intervention shown to favor continuity and reduce time to treatment has been ‘enhancing patient clinical streamlining’ (EPACS), which coordinates patient record-collection, care, expectations and therapy from initial contact. Methods: In this quality improvement project, we implemented EPACS in the breast outpatient service at a specialty cancer hospital. In lieu of the traditional sequence of nursing then doctors' visit followed by coordination of workup and subsequent therapy, EPACS involved an initial nursing visit followed by a telephone call from a provider (registered nurse, RN or advanced practitioner, APP) used to collect medical records/data, clarify patient expectations, and coordinate workup. Patients were then reviewed by MDs and treatment decisions were made. Phone calls were made to a subgroup of patients to assess satisfaction; and participating providers completed questionnaires regarding their experience of the pathway implementation. Results: The EPACS pilot was run at 8 clinics in parallel. In total 156 of 377 eligible patients (41.4%) were telephoned in a 6 month period. RN telephone calls lasted 15-60 minutes; while 82% of APP calls lasted > 60 minutes. 66 of these 156 (42.3%) were questioned on a separate phone call on their experience. Patient and MD satisfaction with EPACS was universally high: patients consistently reported that they felt better prepared and connected to their team after the calls. The majority of MDs felt that EPACS saved them time and increased overall efficiency. RN and APP responses were more mixed, with some feeling that calls were lengthy and overly broad in scope. Notably, only 19% of eligible patients were contacted prior to RN and practice-associated APP visits; vs 74% by per-diem APPs; hinting at time limitations. Cancellation and no-show rates were unchanged vs pre-EPACS, as were patient time in clinic; and time to first chemotherapy from first visit. There was no objective harm to patients with EPACS vs without. Conclusions: Although EPACS implementation in a busy breast outpatient service did not replicate prior findings of increased patient retention or reduced time to treatment; it did result in objective significant improvement in patient experience as judged by patient-reported metrics. Patients felt prepared, involved, and heard, though partially at the cost of staff time and workflow. Future work might aim to lighten the data-gathering aspects of the EPACS framework and focus instead on patient ideas, concerns and expectations, capitalizing on the observed benefits while limiting negative impact on time management experienced by some providers.

Enhancing patient clinical streamlining (EPACS) pilot in the breast medicine service at memorial sloan kettering cancer center.

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Health system-scale language models are all-purpose prediction engines

754 Prediction of 30-day All-Cause Readmission of Neurosurgery Patients Using Large Language Models

Enhancing patient clinical streamlining (EPACS) pilot in the breast medicine service at memorial sloan kettering cancer center.