A recent survey by the Pistoia Alliance has uncovered a burgeoning ‘scientific content crisis’ within the life sciences sector, revealing that incomplete data and inadequate governance are hindering the effective application of artificial intelligence (AI) in research and development. The findings were presented at the Alliance’s annual conference held in Boston, where more than 170 experts from pharmaceuticals, technology, and academia convened to discuss the challenges surrounding AI implementation.
The survey indicated that 27 percent of life science professionals are unaware of the scientific content utilized by their organization’s AI or large language model (LLM) systems, with some relying solely on titles and abstracts. Furthermore, only 36 percent reported incorporating internal documents into their AI models. This lack of comprehensive and traceable evidence is eroding confidence in AI outputs, as many systems are being built on incomplete datasets.
Neal Dunkinson, Senior Director at the Copyright Clearance Center, emphasized the critical nature of these concerns, stating, “It’s clear from discussions at the conference that many AI models are not yet drawing on the full range of scientific evidence needed to deliver authoritative results. Many organizations are still in a learning phase when it comes to both data and governance and, given the stakes for patient safety, that cannot be ignored.”
The survey also revealed that 38 percent of respondents believe their copyright and licensing policies are unclear or inadequately enforced. Dunkinson cautioned that this uncertainty could expose organizations to fines during an already expensive drug development process. “To ensure models are grounded in the highest-quality and most complete scientific datasets, the industry must ensure any datasets being used are AI-ready: meaning properly structured, licensed, and transparent,” he added.
The poll underscored the need for enhanced benchmarking and governance for AI agents, with half of the respondents citing the absence of shared verification standards as the primary barrier to adoption. In response, Robert Gill, Agentic AI programme lead at the Pistoia Alliance, urged conference attendees to participate in the Alliance’s agentic AI project, which aims to establish standards for safe, scalable AI and ensure organizations have complete visibility over the data their AI models utilize.
Several sessions at the conference highlighted practical applications of AI in life sciences. EPAM demonstrated AI-driven methods for streamlining clinical operations, while the Michael J. Fox Foundation showcased the use of knowledge graphs to accelerate Parkinson’s research. AbbVie also discussed how AI is enhancing pharmacovigilance activities.
A roundtable led by Elsevier brought together experts from renowned companies including Eli Lilly, Pfizer, Bayer, J&J, and Takeda to deliberate on the practical implementation of AI tools in real-world research settings. The consensus was that successful AI adoption hinges on problem-led, intuitive design and seamless integration into existing workflows. Representatives from Eli Lilly, Kalleid, Elsevier, Takeda, and Ziffo also noted that the success of AI is heavily reliant on human factors and incentives, echoing findings from Pistoia’s Lab of the Future survey, which indicated that 34 percent of respondents consider a lack of skilled talent a significant barrier to AI adoption.
Dr. Becky Upton, President of the Pistoia Alliance, pointed out the universality of these concerns, noting that similar issues regarding AI trust, transparency, and skills were raised at both the US and European conferences. She stressed that collaboration on common standards, data quality, and practical implementation is essential to advancing the industry with confidence. “The Pistoia Alliance exists to facilitate this collaboration, and we’re excited to carry these discussions into our spring meeting in London,” she concluded.
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