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

Konstantin Hebenstreit1, Constantin Convalexius1, Stephan Reichl1,2

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General-purpose artificial intelligence (GPAI) may double biomedical research speed, with future potential for 25x-100x acceleration. However, realizing this requires overcoming biological, infrastructural, and community adoption challenges.

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Area of Science:

  • Biomedical research
  • Artificial intelligence
  • Scientific discovery

Background:

  • General-purpose artificial intelligence (GPAI) is anticipated to accelerate scientific discovery.
  • The practical limitations of GPAI within the biomedical field are not well understood.

Purpose of the Study:

  • To develop a framework assessing GPAI capabilities across the biomedical research lifecycle.
  • To evaluate the potential speed increases and limitations of GPAI in biomedicine.

Main Methods:

  • A scoping literature review was conducted to identify current and future GPAI capabilities.
  • Expert elicitation with eight senior biomedical researchers was performed to gauge perceived acceleration potential and limitations.

Main Results:

  • Current GPAI offers a ~2x speed increase; future GPAI could yield 25x for physical and 100x for cognitive tasks.
  • Significant limitations include biological constraints, infrastructure, data access, and human oversight.
  • Experts expressed skepticism about accelerating experiment design/execution but found acceleration in manuscript preparation plausible.
  • Community assimilation of new tools was identified as a critical bottleneck.

Conclusions:

  • Achieving substantial GPAI-driven acceleration in biomedicine requires addressing technological, infrastructural, and systemic factors.
  • Targeted investment in shared automation infrastructure and reforms in research/publication practices are essential.
  • Overcoming the human factor in tool adoption is crucial for realizing GPAI's full potential.