Journal of nephrology · 2024
How to improve ChatGPT performance for nephrologists: a technique guide.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Miao Jing, Thongprayoon Charat, Craici Iasmina M, Cheungpasitporn Wisit
Аннотация
BACKGROUND: The integration of ChatGPT into nephrology presents opportunities for enhanced decision-making and patient care. However, refining its performance to meet the specific needs of nephrologists remains a challenge. This guide offers a strategic roadmap for advancing ChatGPT's effectiveness in nephrological applications.
METHODS: Utilizing the advanced capabilities of GPT-4, we customized user profiles to optimize the model's response quality for nephrological inquiries. We assessed the efficacy of chain-of-thought prompting versus standard prompting in delineating the diagnostic pathway for nephrogenic diabetes insipidus-associated hypernatremia and polyuria. Additionally, we explored the influence of integrating retrieval-augmented generation on the model's proficiency in detailing pharmacological interventions to decelerate the progression from chronic kidney disease (CKD) G3 to end-stage kidney disease (ESKD), comparing it to responses without retrieval-augmented generation.
RESULTS: In contrast to the standard prompting, the chain-of-thought method offers a step-by-step diagnostic process that mirrors the intricate thought processes needed for diagnosing nephrogenic diabetes insipidus-related hypernatremia and polyuria. This begins with an initial assessment, notably including a water deprivation test. After evaluating the outcomes of this test, the approach continues by identifying potential causes. Furthermore, if a patient's history suggests lithium usage, the chain-of-thought model adjusts by proposing a more customized course of action. In response to "List medication treatment to help slow progression of CKD G3 to ESKD?", GPT-4 only provides a general summary of medication options. Nevertheless, a specialized GPT-4 model equipped with a retrieval-augmented generation system delivers more precise responses, including renin-angiotensin system inhibitors, sodium-glucose cotransporter-2 inhibitors, and mineralocorticoid receptor antagonists. This aligns well with the 2024 KDIGO guidelines.
CONCLUSIONS: GPT-4, when integrated with chain-of-thought prompting and retrieval-augmented generation techniques, demonstrates enhanced performance in the nephrology domain. This guide underscores the transformative potential of chain-of-thought and retrieval-augmented generation techniques in optimizing ChatGPT for nephrology, and highlights the ongoing need for innovative, tailored AI solutions in specialized medical fields.
METHODS: Utilizing the advanced capabilities of GPT-4, we customized user profiles to optimize the model's response quality for nephrological inquiries. We assessed the efficacy of chain-of-thought prompting versus standard prompting in delineating the diagnostic pathway for nephrogenic diabetes insipidus-associated hypernatremia and polyuria. Additionally, we explored the influence of integrating retrieval-augmented generation on the model's proficiency in detailing pharmacological interventions to decelerate the progression from chronic kidney disease (CKD) G3 to end-stage kidney disease (ESKD), comparing it to responses without retrieval-augmented generation.
RESULTS: In contrast to the standard prompting, the chain-of-thought method offers a step-by-step diagnostic process that mirrors the intricate thought processes needed for diagnosing nephrogenic diabetes insipidus-related hypernatremia and polyuria. This begins with an initial assessment, notably including a water deprivation test. After evaluating the outcomes of this test, the approach continues by identifying potential causes. Furthermore, if a patient's history suggests lithium usage, the chain-of-thought model adjusts by proposing a more customized course of action. In response to "List medication treatment to help slow progression of CKD G3 to ESKD?", GPT-4 only provides a general summary of medication options. Nevertheless, a specialized GPT-4 model equipped with a retrieval-augmented generation system delivers more precise responses, including renin-angiotensin system inhibitors, sodium-glucose cotransporter-2 inhibitors, and mineralocorticoid receptor antagonists. This aligns well with the 2024 KDIGO guidelines.
CONCLUSIONS: GPT-4, when integrated with chain-of-thought prompting and retrieval-augmented generation techniques, demonstrates enhanced performance in the nephrology domain. This guide underscores the transformative potential of chain-of-thought and retrieval-augmented generation techniques in optimizing ChatGPT for nephrology, and highlights the ongoing need for innovative, tailored AI solutions in specialized medical fields.