What are the limits to biomedical research acceleration through general-purpose AI?
- URL: http://arxiv.org/abs/2508.16613v1
- Date: Wed, 13 Aug 2025 10:54:11 GMT
- Title: What are the limits to biomedical research acceleration through general-purpose AI?
- Abstract summary: General-purpose artificial intelligence (GPAI) is widely expected to accelerate scientific discovery.<n>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.
- Score: 2.31014638945732
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- The Biosecurity Blind Spot: Systematic Dual-use Detection in Open Science Infrastructure [0.6124773188525718]
We present the first systematic analysis of dual-use research of concern (DURC) content on open preprint servers.<n>Our analysis reveals that dual-use-adjacent knowledge is routinely present in openly accessible titles and abstracts.<n>We argue that institutional review processes, funding requirements, and preprint platform policies must evolve to incorporate proactive, metadata-level monitoring.
arXiv Detail & Related papers (2026-05-10T16:17:41Z) - SciResearcher: Scaling Deep Research Agents for Frontier Scientific Reasoning [54.194672921128785]
We introduce SciResearcher, a fully automated agentic framework for frontier-science data construction.<n>SciResearcher synthesizes diverse conceptual and computational tasks grounded in academic evidence.<n>We develop SciResearcher-8B, an agent foundation model that achieves 19.46% on the HLE-Bio/Chem-Gold benchmark.
arXiv Detail & Related papers (2026-05-02T15:26:45Z) - Towards a Medical AI Scientist [73.6056699962416]
We introduce Medical AI Scientist, the first autonomous research framework tailored to clinical autonomous research.<n>The framework operates under 3 research modes, namely paper-based reproduction, literature-inspired innovation, and task-driven exploration.<n>Our system achieves strong alignment between the proposed method and its implementation, while also demonstrating significantly higher success rates in executable experiments.
arXiv Detail & Related papers (2026-03-30T15:37:25Z) - EWE: An Agentic Framework for Extreme Weather Analysis [61.092871317626496]
Extreme Weather Expert (EWE) is first intelligent agent framework dedicated to this task.<n>EWE emulates expert visualizations through knowledge-guided planning, closed-loop reasoning, and a domain-tailored meteorological toolkit.<n>To catalyze progress, we introduce the first benchmark for this emerging field, comprising a curated dataset of 103 high-impact events.
arXiv Detail & Related papers (2025-11-26T14:37:25Z) - Jr. AI Scientist and Its Risk Report: Autonomous Scientific Exploration from a Baseline Paper [23.009743151474638]
Jr. AI Scientist is a state-of-the-art autonomous AI scientist system that mimics the core research workflow of a novice student researcher.<n>It generates new research papers that build upon real NeurIPS, IJCV, and ICLR works by proposing and implementing novel methods.
arXiv Detail & Related papers (2025-11-06T17:37:49Z) - AI Scientists Fail Without Strong Implementation Capability [33.232300349142285]
The emergence of Artificial Intelligence (AI) Scientist represents a paradigm shift in scientific discovery.<n>Recent AI Scientist studies demonstrate sufficient capabilities for independent scientific discovery.<n>Despite this substantial progress, AI Scientist has yet to produce a groundbreaking achievement in the domain of computer science.
arXiv Detail & Related papers (2025-06-02T06:59:10Z) - Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences [50.9036832382286]
We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability.<n>We discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI.<n>Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI.
arXiv Detail & Related papers (2025-05-22T12:52:34Z) - Scaling Laws in Scientific Discovery with AI and Robot Scientists [72.3420699173245]
An autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle.<n>AGS aims to significantly reduce the time and resources needed for scientific discovery.<n>As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws.
arXiv Detail & Related papers (2025-03-28T14:00:27Z) - Unlocking the Potential of AI Researchers in Scientific Discovery: What Is Missing? [20.94708392671015]
We project that AI4Science's share of total publications will rise from 3.57% in 2024 to approximately 25% by 2050.<n>We propose structured and actionable strategies to position AI researchers at the forefront of scientific discovery.
arXiv Detail & Related papers (2025-03-05T09:29:05Z) - DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery
through Sophisticated AI System Technologies [116.09762105379241]
DeepSpeed4Science aims to build unique capabilities through AI system technology innovations.
We showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.
arXiv Detail & Related papers (2023-10-06T22:05:15Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks [68.39821375903591]
Generalist AI holds the potential to address limitations due to its versatility in interpreting different data types.
Here, we propose BiomedGPT, the first open-source and lightweight vision-language foundation model.
arXiv Detail & Related papers (2023-05-26T17:14:43Z) - Accelerating COVID-19 research with graph mining and transformer-based
learning [2.493740042317776]
We present an automated general purpose hypothesis generation systems AGATHA-C and AGATHA-GP for COVID-19 research.
Both systems achieve high-quality predictions across domains (in some domains up to 0.97% ROC AUC) in fast computational time.
We show that the systems are able to discover on-going research findings such as the relationship between COVID-19 and oxytocin hormone.
arXiv Detail & Related papers (2021-02-10T15:11:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.