The rapid emergence of AI has been met with equal parts enthusiasm and skepticism in the life sciences industry – while an estimated 32% of life sciences organizations have adopted Generative AI in their organizations, only 5% view AI as a competitive advantage.
Much of the hesitation towards using generic GenAI for life sciences mirrors the same sentiment when SaaS and cloud software first emerged. That is, while horizontal solutions excel at supporting many industries, they fall short in complex domains like life sciences.
When it comes to AI, effectively leveraging this technology in life sciences presents unique challenges. For one, the volume of life sciences data has exploded over the last decade – and is expected to reach a 36% CAGR by 2025. Even as AI’s computing power becomes faster and cheaper, accessing and structuring this information will continue to pose a challenge. That's because AI must not only ingest this massive amount of data but also correctly interpret this information and output accurate results. To do so requires AI models that have deep domain expertise – from complex regulatory requirements to specialized scientific knowledge.
And while today's generic models are more advanced than ever, they still hallucinate up to 27% of the time - even more frequently in life sciences scenarios. A recent University of Massachusetts Amherst study found that medical summaries produced by leading models like GPT-4 and Llama-3 contained hallucinations in nearly all outputs from a sample of just 100 medical papers. When it comes to decisions and insights that impact patient lives, there's no room for error.
Beyond accuracy, security and compliance are essential when ingesting patient and proprietary healthcare data. Life sciences companies must use tools built specifically for their regulations, with robust security and privacy safeguards.
Life Sciences vertical AI solutions are already demonstrating remarkable capabilities. Rather than simply adding AI as a layer on top of existing systems, these specialized platforms are built from the ground up, applying deep life sciences expertise to transform data into insights.
This transformation typically begins with comprehensive data ingestion. Advanced vertical AI solutions can connect with dozens of different data sources across internal and external locations – from publications to CRM to clinical trial data – eliminating silos and establishing a comprehensive foundation for insights. These platforms can even extract data from unstructured sources, like PDFs and conference presentations, turning these unstructured but highly valuable assets into usable data points.
After ingesting data from numerous sources, vertical AI systems enrich and structure this information through a combination of different tactics like medically-tuned ontologies, LLMs, and data labeling to accurately categorize information and connect related concepts. Because these solutions are trained on life sciences knowledge from the start, they can understand and connect related scientific terms and concepts in ways generic tools simply cannot achieve.
The most advanced vertical AI solutions go beyond just organizing data – they use generative AI to help teams uncover critical insights hidden within their data. Lastly, to ensure compliance with life sciences regulatory standards, Vertical AI solutions must come pre-built with the necessary data privacy and compliance controls, and provide a dedicated, secure tenant to customers.
Demonstrating that there is a solid product-market fit is at the top of the list. As vertical AI solutions continue to gain momentum, they're delivering exponentially better results than even vertical SaaS products. That's because AI is built from a foundation of intelligence, not process or workflows. While traditional SaaS products – even vertical ones – confine teams to specific, pre-built processes, AI works differently. By first establishing a deep foundation of knowledge, AI can then enable various tasks and workflows – functioning as a true extension of your team rather than a rigid system.
A common example to illustrate the difference between AI and SaaS? Without AI, when medical affairs teams need to understand an HCP's stance on a new therapy, they typically have to jump between various systems and sources – from CRMs and congress notes to publications – to piece together the complete story. After all, even the best-maintained CRMs only capture part of an HCP’s full point of view.
AI transforms this fragmented approach by connecting insights across all relevant sources, including unstructured formats where key HCP information often resides. While SaaS may lose these critical insights, with AI’s intelligent foundation, this unstructured, scattered information becomes usable data for teams to quickly leverage.
With this intelligence-first strategy, Sorcero is demonstrating how vertical AI can deliver remarkable results for the life sciences industry.