H1 Launches Gen AI Tool to Accelerate Clinical Trials

What You Should Know:

• H1, the leading source of truth for global Healthcare Professional, clinical, scientific, and research information, today announced the launch of GenosAI, a new generative AI tool seamlessly embedded into its clinical trial intelligence platform, Trial Landscape, to analyze and respond to all types of complex inquiries. 
• Trial Landscape allows sponsors to identify new sites, centers of excellence, and investigators with diverse patients; evaluate site and PI patient recruitment strategies; and gauge competition across trials. GenosAI is a significant leap forward in enhancing efficiency, diversity, and innovation in the pharmaceutical industry. 

AI-Driven Platforms to Analyze Complex Inquiries

Deloitte suggests that AI has the potential to slash drug discovery costs by up to 70%. GenosAI harnesses its advanced data analysis capabilities to empower clinical researchers, enabling them to make quicker and more accurate decisions than ever before. With GenosAI, clinical operations, feasibility assessments, and medical teams can easily access profound insights, expedite workflows, explore novel data sources, enhance efficiency, reduce expenses, and accelerate the time it takes to bring products to market.

The Trial Landscape is a comprehensive repository of clinical trial data, drawing insights from over 490,000 trials, 25 million publications, 10 million healthcare providers (including those with diversity data), 240 million patients, and 11 billion global claims. Clients also have the option to integrate their own data, such as historical trial performance and feasibility questionnaire responses. GenosAI leverages this data and employs natural language processing (NLP) capabilities to decipher and respond to advanced analytical queries, offering an experience akin to ChatGPT. Users can pose questions such as “Which sponsors are leading the most promising small cell prostate cancer trials?” or “What are the top-performing sites for my phase 3 prostate cancer study?” and receive highly reliable, actionable responses within seconds.

“GenosAI has the power to be a pharma company’s most impactful Large Language Model (LLM) tool,” said Ariel Katz, CEO and co-founder, H1. “With GenosAI, we’ve combined the power of conversational AI with our unmatched breadth of healthcare data to give sponsors access to the best data and technology possible. We hope this will have a profound impact on clinical trial diversity and efficacy.”

GenosAI enhances the landscape of clinical research in the following manners:

1. Enhancing Competitive Intelligence & Trial Design: GenosAI provides valuable insights into competing trials, optimizing the strategies for designing clinical trials.

2. Streamlining Site Selection & Feasibility Assessment: GenosAI swiftly evaluates the performance and feasibility of clinical trial sites, assisting in the identification of the most suitable locations.

3. Promoting Diversity & Inclusion: GenosAI aids in achieving diversity and inclusion objectives in clinical trials, ensuring adequate representation of minority populations.

4. Improving Clinical Trial Design: By analyzing extensive medical literature and trial data, GenosAI identifies sites and Principal Investigators (PIs) that align with specific inclusion and exclusion criteria, thus enhancing the design of clinical trials.

5. Breaking Down Internal Clinical Trial Data Barriers: GenosAI has the capability to work with client data sources, breaking down historically isolated data silos to provide valuable insights.

GenosAI operates on H1’s global data platform, harnessing cutting-edge technologies such as machine learning (ML) and natural language processing (NLP) to establish a comprehensive, unified source of healthcare data. The platform prioritizes data privacy and security through the utilization of Microsoft Azure Enterprise GPT-4 and employs guardrails to mitigate common healthcare language model hallucinations. Additionally, the Retrieval Augmented Generation (RAG) approach enriches the model with H1-specific data and knowledge.

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