ChatGPT Performs on the Chinese National Medical Licensing Examination.

ChatGPT, a language model developed by OpenAI, uses a 175 billion parameter Transformer architecture for natural language processing tasks. This study aimed to compare the knowledge and interpretation ability of ChatGPT with those of medical students in China by administering the Chinese National Medical Licensing Examination (NMLE) to both ChatGPT and medical students. We evaluated the performance of ChatGPT in three years' worth of the NMLE, which consists of four units. At the same time, the exam results were compared to those of medical students who had studied for five years at medical colleges. ChatGPT's performance was lower than that of the medical students, and ChatGPT's correct answer rate was related to the year in which the exam questions were released. ChatGPT's knowledge and interpretation ability for the NMLE were not yet comparable to those of medical students in China. It is probable that these abilities will improve through deep learning.

Creation and Adoption of Large Language Models in Medicine.

Importance: There is increased interest in and potential benefits from using large language models (LLMs) in medicine. However, by simply wondering how the LLMs and the applications powered by them will reshape medicine instead of getting actively involved, the agency in shaping how these tools can be used in medicine is lost. Observations: Applications powered by LLMs are increasingly used to perform medical tasks without the underlying language model being trained on medical records and without verifying their purported benefit in performing those tasks. Conclusions and Relevance: The creation and use of LLMs in medicine need to be actively shaped by provisioning relevant training data, specifying the desired benefits, and evaluating the benefits via testing in real-world deployments.

Large language models encode clinical knowledge.

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a benchmark combining six existing medical question answering datasets spanning professional medicine, research and consumer queries and a new dataset of medical questions searched online, HealthSearchQA. We propose a human evaluation framework for model answers along multiple axes including factuality, comprehension, reasoning, possible harm and bias. In addition, we evaluate Pathways Language Model1 (PaLM, a 540-billion parameter LLM) and its instruction-tuned variant, Flan-PaLM2 on MultiMedQA. Using a combination of prompting strategies, Flan-PaLM achieves state-of-the-art accuracy on every MultiMedQA multiple-choice dataset (MedQA3, MedMCQA4, PubMedQA5 and Measuring Massive Multitask Language Understanding (MMLU) clinical topics6), including 67.6% accuracy on MedQA (US Medical Licensing Exam-style questions), surpassing the prior state of the art by more than 17%. However, human evaluation reveals key gaps. To resolve this, we introduce instruction prompt tuning, a parameter-efficient approach for aligning LLMs to new domains using a few exemplars. The resulting model, Med-PaLM, performs encouragingly, but remains inferior to clinicians. We show that comprehension, knowledge recall and reasoning improve with model scale and instruction prompt tuning, suggesting the potential utility of LLMs in medicine. Our human evaluations reveal limitations of today's models, reinforcing the importance of both evaluation frameworks and method development in creating safe, helpful LLMs for clinical applications.

It Takes an Average of 17 Years for Evidence to Change Practice-the Burgeoning Field of Implementation Science Seeks to Speed Things Up.

This Medical News feature discusses how implementation science seeks to narrow the often yearslong gap between the development of evidence-based practices and their routine use in the real world.

Medical School Rankings-Bad for the Health of the Profession and the Public.

This Viewpoint discusses the limitations of medical school ranking in attracting a diverse student population and urges administrators to holistically communicate their mission, goals, and learning environment as an alternative strategy.

Ensino do exame respiratório: o que é história e o que é necessidade? / Teaching the respiratory physical examination: what is history and what is needed?

A semiologia é uma das técnicas mais utilizadas na prática médica há séculos. Ensinada por meio de roteiros sistematizados, estudantes de inúmeras escolas da área de saúde por todo o mundo aprendem as manobras semiológicas como fundamento na avaliação dos pacientes. No entanto, apesar de extremamente difundida, discute-se pouco sobre sua acurácia como manobra diagnóstica. Tendo este ponto em vista, este artigo aborda a precisão das diversas manobras semiológicas do exame físico do aparelho respiratório e a descrição comparativa do seu ensino em diferentes escolas médicas no mundo. Como resultados, tem-se valores de acurácia discordantes, o que pode ser justificado pela qualidade dos estudos ou pelas variáveis analisadas que diferem entre os estudos e propostas de padronização. Em conclusão, a semiologia é a base da avaliação médica, independentemente dos avanços e disponibilidade dos exames de imagens, e cada manobra deve ser ensinada com seu devido valor científico. Conhecer a aplicabilidade e individualizar a prática das etapas do exame respiratório pode ser um caminho possível de adequação aos tempos atuais, sem impor perdas de informações relevantes para o desenvolvimento do raciocínio clínico (AU)

Medical semiology has been one of the most common techniques used in medical practice for centuries. Health science students around the globe learn these techniques through a systematized model as a fundamental skill for patient evaluation. However, though being widespread, little is known about semiology's true accuracy as a diagnostic maneuver. Knowing that, through a literature review, this paper evaluated the precision of the preconized procedures that are used as part of the exam of the respiratory system and the comparative description of its teaching in different medical schools around the world. As a result, disagreement between several papers was found, which can be justified by the poor quality of the studies and the different variables that were studied in each one. However, one thing is still clear: respiratory physical examination continues to be essential in medical practice, independently of the recent advances and availability of imaging exams. Teaching each step should consider available scientific evidence. The knowledge of the applicability and practical individualization of the respiratory examination can be a possible way for the current times without missing relevant information for developing clinical reasoning (AU)