# Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/diagnostic-reasoning-prompts-reveal-the-potential-for-large-language-model/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Thomas Savage,Ashwin Nayak,Robert Gallo,Ekanath Rangan,Jonathan H. Chen |
| Citations | 250 |
| DOI | 10.1038/s41746-024-01010-1 |
| Fields | Computer Science,Medicine |
| Open Access | true |
| OA Status | gold |
| OA URL | https://www.nature.com/articles/s41746-024-01010-1.pdf |
| OpenAlex ID | https://openalex.org/W4391170193 |
| PMID | 38267608 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Jonathan H. Chen](https://scholariq.org/researchers/jonathan-h-chen/)

## Paper journal

- [npj Digital Medicine](https://scholariq.org/journals/npj-digital-medicine/)

## Paper primary topic

- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)

## Paper topics

- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)
- [Topic Modeling](https://scholariq.org/topics/topic-modeling/)
- [Machine Learning in Healthcare](https://scholariq.org/topics/machine-learning-in-healthcare/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.