UNASSIGNED: Large language models (LLMs) have a high diagnostic accuracy when they evaluate previously published clinical cases.
UNASSIGNED: We compared the accuracy of GPT-4\'s differential diagnoses for previously unpublished challenging case scenarios with the diagnostic accuracy for previously published cases.
UNASSIGNED: For a set of previously unpublished challenging clinical cases, GPT-4 achieved 61.1% correct in its top 6 diagnoses versus the previously reported 49.1% for physicians. For a set of 45 clinical vignettes of more common clinical scenarios, GPT-4 included the correct diagnosis in its top 3 diagnoses 100% of the time versus the previously reported 84.3% for physicians.
UNASSIGNED: GPT-4 performs at a level at least as good as, if not better than, that of experienced physicians on highly challenging cases in internal medicine. The extraordinary performance of GPT-4 on diagnosing common clinical scenarios could be explained in part by the fact that these cases were previously published and may have been included in the training dataset for this LLM.

UNASSIGNED: Given the strikingly high diagnostic error rate in hospitals, and the recent development of Large Language Models (LLMs), we set out to measure the diagnostic sensitivity of two popular LLMs: GPT-4 and PaLM2. Small-scale studies to evaluate the diagnostic ability of LLMs have shown promising results, with GPT-4 demonstrating high accuracy in diagnosing test cases. However, larger evaluations on real electronic patient data are needed to provide more reliable estimates.
UNASSIGNED: To fill this gap in the literature, we used a deidentified Electronic Health Record (EHR) data set of about 300,000 patients admitted to the Beth Israel Deaconess Medical Center in Boston. This data set contained blood, imaging, microbiology and vital sign information as well as the patients\' medical diagnostic codes. Based on the available EHR data, doctors curated a set of diagnoses for each patient, which we will refer to as ground truth diagnoses. We then designed carefully-written prompts to get patient diagnostic predictions from the LLMs and compared this to the ground truth diagnoses in a random sample of 1000 patients.
UNASSIGNED: Based on the proportion of correctly predicted ground truth diagnoses, we estimated the diagnostic hit rate of GPT-4 to be 93.9%. PaLM2 achieved 84.7% on the same data set. On these 1000 randomly selected EHRs, GPT-4 correctly identified 1116 unique diagnoses.
UNASSIGNED: The results suggest that artificial intelligence (AI) has the potential when working alongside clinicians to reduce cognitive errors which lead to hundreds of thousands of misdiagnoses every year. However, human oversight of AI remains essential: LLMs cannot replace clinicians, especially when it comes to human understanding and empathy. Furthermore, a significant number of challenges in incorporating AI into health care exist, including ethical, liability and regulatory barriers.

OBJECTIVE: The Liver Imaging Reporting and Data System (LI-RADS) offers a standardized approach for imaging hepatocellular carcinoma. However, the diverse styles and structures of radiology reports complicate automatic data extraction. Large language models hold the potential for structured data extraction from free-text reports. Our objective was to evaluate the performance of Generative Pre-trained Transformer (GPT)-4 in extracting LI-RADS features and categories from free-text liver magnetic resonance imaging (MRI) reports.
METHODS: Three radiologists generated 160 fictitious free-text liver MRI reports written in Korean and English, simulating real-world practice. Of these, 20 were used for prompt engineering, and 140 formed the internal test cohort. Seventy-two genuine reports, authored by 17 radiologists were collected and de-identified for the external test cohort. LI-RADS features were extracted using GPT-4, with a Python script calculating categories. Accuracies in each test cohort were compared.
RESULTS: On the external test, the accuracy for the extraction of major LI-RADS features, which encompass size, nonrim arterial phase hyperenhancement, nonperipheral \'washout\', enhancing \'capsule\' and threshold growth, ranged from .92 to .99. For the rest of the LI-RADS features, the accuracy ranged from .86 to .97. For the LI-RADS category, the model showed an accuracy of .85 (95% CI: .76, .93).
CONCLUSIONS: GPT-4 shows promise in extracting LI-RADS features, yet further refinement of its prompting strategy and advancements in its neural network architecture are crucial for reliable use in processing complex real-world MRI reports.

OBJECTIVE: Effective clinical event classification is essential for clinical research and quality improvement. The validation of artificial intelligence (AI) models like Generative Pre-trained Transformer 4 (GPT-4) for this task and comparison with conventional methods remains unexplored.
METHODS: We evaluated the performance of the GPT-4 model for classifying gastrointestinal (GI) bleeding episodes from 200 medical discharge summaries and compared the results with human review and an International Classification of Diseases (ICD) code-based system. The analysis included accuracy, sensitivity, and specificity evaluation, using ground truth determined by physician reviewers.
RESULTS: GPT-4 exhibited an accuracy of 94.4% in identifying GI bleeding occurrences, outperforming ICD codes (accuracy 63.5%, P < 0.001). GPT-4\'s accuracy was either slightly lower or statistically similar to individual human reviewers (Reviewer 1: 98.5%, P < 0.001; Reviewer 2: 90.8%, P = 0.170). For location classification, GPT-4 achieved accuracies of 81.7% and 83.5% for confirmed and probable GI bleeding locations, respectively, with figures that were either slightly lower or comparable with those of human reviewers. GPT-4 was highly efficient, analyzing the dataset in 12.7 min at a cost of 21.2 USD, whereas human reviewers required 8-9 h each.
CONCLUSIONS: Our study indicates GPT-4 offers a reliable, cost-efficient, and faster alternative to current clinical event classification methods, outperforming the conventional ICD coding system and performing comparably to individual expert human reviewers. Its implementation could facilitate more accurate and granular clinical research and quality audits. Future research should explore scalability, prompt and model tuning, and ethical implications of high-performance AI models in clinical data processing.

ChatGPT is a new artificial intelligence-powered language model of chatbot able to help otolaryngologists in clinical practice and research. We investigated the ability of ChatGPT-4 in the editing of a manuscript in otolaryngology. Four papers were written by a nonnative English otolaryngologist and edited by a professional editing service. ChatGPT-4 was used to detect and correct errors in manuscripts. From the 171 errors in the manuscripts, ChatGPT-4 detected 86 errors (50.3%) including vocabulary (N = 36), determiner (N = 27), preposition (N = 24), capitalization (N = 20), and number (N = 11). ChatGPT-4 proposed appropriate corrections for 72 (83.7%) errors, while some errors were poorly detected (eg, capitalization [5%] and vocabulary [44.4%] errors. ChatGPT-4 claimed to change something that was already there in 82 cases. ChatGPT demonstrated usefulness in identifying some types of errors but not all. Nonnative English researchers should be aware of the current limits of ChatGPT-4 in the proofreading of manuscripts.