The need for the adoption of AI/ML in Life science companies has increased as the Industry evolves and moves towards high tech cures and precision medicine that is based on specific substances and production methods. The entire value chain is broken down and executed by specialist providers. The Pharma company creates the formula ,production is done by specialized contract manufacturers, packaged by specialized companies, medical distributors storing the drugs at specialized conditions before reaching the pharmacies and patients. Managing this complex process for thousands of therapies makes it imperative for Lifesciences companies to start their AI/ML journey at the earliest.
There are varied use cases being adopted by companies across the value streams starting from Drug discovery, Clinical trials , Manufacturing, Supply chain, Commercial to Post market surveillance. By adopting and implementing AI/ML Life science companies can expedite drug discovery and development process , Manufacturing more efficiently , make supply chains more responsive, and reduce compliance risks.
Challenges Faced In AI/ML adoption
Some of the Challenges faced by the Life Science companies are :
Poor Data Quality : Data is usually stored in different ways, in different formats and varies across systems. So, getting a clean dataset for machine learning models is a challenge that needs to be addressed.
Data silos: Siloed data systems that involves various Lifesciences companies like Pharma, Health Care providers, Contract Manufacturers, Pharmacieset care a major issue and diminish the value of the available data.
Risk Management – The Datasets from individual data sets may be compliant with regulatory needs but when combining multiple sources the companies should not be compromising the identity of patient involved.
Trust and Explainability: Due to long term effects of biased and incorrect models , the models cannot be a black box to clinicians , Health care practitioners and regulatory approvers.
Call to Action
Once the Leaders have defined the Vision and Strategy, companies need to put in place the following building blocks at a minimum
Data Standardization: Companies should invest in End to End data standardization. Data should be standardized right from clinical development to post market surveillance between all partners involved that will ensure traceability and regulatory compliance
Technology backbone: One of the critical failure points is due to the data silos and disparate systems. Companies should invest in ensuring seamless integration of data elements between the various systems involved in the end to end process. The company should also be investing in having the right IT architecture and infrastructure assets that can scale to future organizational needs
Compliance and Risk Management :Companies should invest in rehauling the compliance and risk management strategy that help reduce bias and increase the transparency of the model . The Audit strategy should protect the company from regulatory risks that could be caused by usage of AI models
