Articlenpj biomedical innovations2025
PodGPT: an audio-augmented large language model for research and education.
Article in npj biomedical innovations, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Performance of open-source large language models on nephrology self-assessment program.medRxiv : the preprint server for health sciences · 2026Article
- AI-augmented frameworks for enhancing Alzheimer's disease clinical trials: A memory clinic perspective.The journal of prevention of Alzheimer's disease · 2025Article
- From large language models to multimodal AI: a scoping review on the potential of generative AI in medicine.Biomedical engineering letters · 2025Review
Corrections and comments
- Update of
Authors and funding
11 authors.
Funding
Abstract
The proliferation of scientific podcasts has generated an extensive repository of educational content, rich in specialized terminology, diverse topics, and expert dialogues. Here, we introduce a computational framework designed to enhance large language models by leveraging this informational content from publicly accessible audio podcasts across science, technology, engineering, mathematics, and medicine (STEMM). This dataset, comprising over 3700 hours of audio content, was transcribed to generate over 42 million text tokens. Our model, PodGPT, integrates this wealth of complex dialogue found in audio podcasts to improve understanding of natural language nuances, cultural contexts, as well as scientific and medical knowledge. PodGPT also employs retrieval augmented generation (RAG) on a vector database, providing real-time access to emerging scientific literature. Evaluated on multiple benchmarks, PodGPT demonstrated an average improvement of 1.82 percentage points over standard open-source benchmarks and 2.43 percentage points when augmented with evidence from the RAG pipeline. Moreover, it showcased an average improvement of 1.18 percentage points in its zero-shot multilingual transfer ability, effectively generalizing to different linguistic contexts. By harnessing the untapped potential of podcast content, PodGPT advances natural language processing and conversational AI, offering enhanced capabilities for STEMM research and education.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.