What are the limits to biomedical research acceleration through general-purpose AI?

TL;DR

This paper evaluates the potential and limitations of general-purpose AI to accelerate biomedical research, highlighting realistic speedups, bottlenecks, and necessary systemic reforms for effective implementation.

Contribution

It develops a framework to assess GPAI capabilities in biomedicine and combines literature review with expert opinions to identify practical limits and key bottlenecks.

Findings

Current GPAI could double research speed.

Future GPAI may achieve 25x physical and 100x cognitive task acceleration.

Community adoption and systemic reforms are critical for realizing GPAI benefits.

Abstract

Although general-purpose artificial intelligence (GPAI) is widely expected to accelerate scientific discovery, its practical limits in biomedicine remain unclear. We assess this potential by developing a framework of GPAI capabilities across the biomedical research lifecycle. Our scoping literature review indicates that current GPAI could deliver a speed increase of around 2x, whereas future GPAI could facilitate strong acceleration of up to 25x for physical tasks and 100x for cognitive tasks. However, achieving these gains may be severely limited by factors such as irreducible biological constraints, research infrastructure, data access, and the need for human oversight. Our expert elicitation with eight senior biomedical researchers revealed skepticism regarding the strong acceleration of tasks such as experiment design and execution. In contrast, strong acceleration of manuscript preparation, review and publication processes was deemed plausible. Notably, all experts identified the assimilation of new tools by the scientific community as a critical bottleneck. Realising the potential of GPAI will therefore require more than technological progress; it demands targeted investment in shared automation infrastructure and systemic reforms to research and publication practices.