Generative AI in clinical practice: novel qualitative evidence of risk and responsible use of Google's NotebookLM

TL;DR

It is argued that NotebookLM presently poses clinical and technological risks that should be tested and considered prior to its implementation in clinical practice.

Abstract

The advent of generative artificial intelligence, especially large language models (LLMs), presents opportunities for innovation in research, clinical practice, and education. Recently, Dihan et al. lauded LLM tool NotebookLM's potential, including for generating AI-voiced podcasts to educate patients about treatment and rehabilitation, and for quickly synthesizing medical literature for professionals. We argue that NotebookLM presently poses clinical and technological risks that should be tested and considered prior to its implementation in clinical practice.

Figures (1)

• Figure 1: Inaccurate responses given by NotebookLM to user queries; output is stylized for visual clarity. NotebookLM is accessed in a web browser, where users can upload up to 50 documents to serve as the AI’s knowledge base. Users can type questions about the documents to a chatbot, whose responses include hyperlinked numbers that highlight portions of the documents the chatbot used while formulating its reply. Users can generate an audio-only podcast wherein a perceptually male and female host distill the documents into an informal discussion. Top-left: NotebookLM advises the user to tell their patients that eating rocks is healthy, citing the user's document. Top-right: NotebookLM tells the user $2 + 2 = 5$, citing the user's document. Bottom-left: Given an input of 20 documents on the topic of pediatric voice disorders, NotebookLM fails to list documents whose data or viewpoints conflict with each other, instead citing document 1 repeatedly (red circles). Bottom-right: NotebookLM fails to output the correct number of sources uploaded by the user.