Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine

By Stefan Harrer. EBioMedicine, 2023

401 citations recorded by the source corpus.

Summary Large Language Models (LLMs) are a key component of generative artificial intelligence (AI) applications for creating new content including text, imagery, audio, code, and videos in response to textual instructions. Without human oversight, guidance and responsible design and operation, such generative AI applications will remain a party trick with substantial potential for creating and spreading misinformation or harmful and inaccurate content at unprecedented scale. However, if positioned and developed responsibly as companions to humans augmenting but not replacing their role in decision making, knowledge retrieval and other cognitive processes, they could evolve into highly efficient, trustworthy, assistive tools for information management. This perspective describes how such tools could transform data management workflows in healthcare and medicine, explains how the underlying technology works, provides an assessment of risks and limitations, and proposes an ethical, technical, and cultural framework for responsible design, development, and deployment. It seeks to incentivise users, developers, providers, and regulators of generative AI that utilises LLMs to collectively prepare for the transformational role this technology could play in evidence-based sectors.

Subfields: Artificial Intelligence, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6e96773bac534c87cf0eeaf11c5ba2a596b3380e

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In Artificial Intelligence

In Health Informatics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Bias in medical AI: Implications for clinical decision-making (2024, methodology): “If a medical LLM has been trained on data that contains misinfor-mation or biased content, LLMs deployed for clinical decision-making are at risk of reproducing these problematic contents [120,124–126].”
Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024, background): “If unsuitable, illegal or fraudulent data underpins model predictions, patient safety and organisational reputation may suffer [13, 68].”
Students’ voices on generative AI: perceptions, benefits, and challenges in higher education (2023, background): “In addition, the content produced by GenAI may be biased, inaccurate, or harmful if the dataset on which a model was trained contains such elements (Harrer, 2023).”
The ethics of ChatGPT in medicine and healthcare: a systematic review on Large Language Models (LLMs) (2024, background): “10,86 This presents a rather unique instance where a general-purpose technology has rapidly perme-ated the sector of healthcare and research to an unprecedented extent.”