H E A L T H I N S I G H T S
When applying AI to labour-intensive Safety and Regulatory processes in life sciences R & D , the technology up to now has had to be extensively ‘ trained ’ in what to look for , and extensively validated – one application at a time . AI-based conclusions also need to be ‘ explainable ’ to regulators , for the sake of compliance , credibility and trust . All of these ‘ overheads ’ have restricted companies ’ ability to fully exploit the technology .
That has changed now , thanks to the latest advances in Artificial Intelligence ( AI ) and Machine Learning ( ML ) which offer substantial process transformation potential without the same training , validation and explainability burden .
What ’ s changed ?
Generative AI ( GenAI ) technology , using large language models ( LLMs – the vast data banks referred to by GenAI tools ), quickly understands what to look out for and can reliably summarise key findings for the user , without the need for painstaking ‘ training ’ by overstretched teams or validation of each configuration .
In conjunction with advanced natural language processing ( NLP ) techniques like retrieval-augmented generation ( RAG ), LLMs make advanced automation a safe , reliable and efficient reality in key life sciences R & D processes . RAG simplifies the process of fine-tuning AI models by allowing LLMs to integrate proprietary data with publicly-available information , giving them a bigger pool of knowledge – and context – to draw from .
In everyday drug development , Generative AI tools are aiding product safety and regulatory compliance . They help overcome Machine Learning barriers by reducing the ‘ training ’ and system validation burden . This is achieved through on-the-fly data discovery , contextual learning , and narrative extrapolation . Ramesh Ramani , VP of Technology at ArisGlobal , and RaviKanth Valigari , VP of Product Development at ArisGlobal discuss the potential .
Specialised applications can now be developed that apply GenAI-type techniques , contextually , to data they haven ’ t seen before – learning from and processing the contents on the fly . For drug developers , this has the potential to transform everything from dynamic data extraction associated with adverse event ( AE ) intake , to safety case narrative generation , narrative theme analysis in safety signal detection , and the drafting of safety reports .
Carefully combining LLM and RAG capabilities ensures transparency and explainability , meeting regulatory standards for safety and reliability . Responsible AI and compliance are critical in life sciences , so deploying proven , transparent solutions is essential . The LLM / RAG approach addresses potential concerns about data security and privacy , too , as it does not require the use of potentially-sensitive patient data for algorithm training / ML . It also stands up to validation , by way of periodic sampling
GENAI IN LIFE SCIENCES :
HOW LARGE LANGUAGE MODELS WILL TRANSFORM SAFETY AND REGULATORY PROCESSES
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