The pharmaceutical industry is enveloped in excitement around applying GenAI to accelerate drug discovery and introduction of new therapeutics to the market. As summarised by McKinsey & Co and others, AI implementation across life science domains from early research and discovery to clinical development and regulatory to operations, commercial, and medical affairs, to post-market surveillance offer tantalising potential improvements in efficiency, quality, and cost savings. Importantly, the key limiting factors of data quality challenges and the necessity for human validation are repeatedly highlighted as essential components of any GenAI strategy.
For example, real world medical data holds tremendous promise for improving the identification of the most promising disease indications for novel drug molecules and selecting the clinical trial patients who are most likely to benefit. However, the complexity and inconsistency of these data have limited their utilisation. Having successfully dealt with such complexities, we attest to the necessity for expert human-oversight data quality management and results validation, which are fundamental to QuadraticMed’s approach and unlock the power of large language models for accurate and trustworthy use of these data.
To quote McKinsey & Co,
“The effectiveness of gen AI depends on the quality of an organisation’s data.”
To realise the potential gains of GenAI across the pharmaceutical value chain, this revolutionary technology must be implemented correctly, requiring high-quality data with expert human oversight to ensure accuracy and reliable decision-making.