Throughout the year, I dedicate time to dissecting the ideas and working practices I believe are revolutionizing the sector. I wanted to share my thoughts on the impact artificial intelligence (AI) and machine learning (ML) are set to have on the industry this year.
In 2023, the AI healthcare market is predicted to reach a value of $20.6 bn, nearly double its $11.1 bn valuation in 2021 according to Precedence Research.
While the technology is relatively longstanding, the sector is only just beginning to understand its potential implications across all business functions, none more so than drug discovery and R&D.
The cost of R&D continues to be exorbitant, with the average timeframe for a project in the region of 12-18 years and the fail rate remaining at around 90%. It is one of the riskiest areas for biopharma organizations but also a field that is vital for their continued success.
AI and ML have the potential to reduce the cost, time, and fail rate of R&D projects by using data and algorithms to predict which leads are the most viable, essentially creating the same number of leads with far fewer experiments, time, money, and research.
Currently, there are few AI healthcare start-ups with their own pipelines instead many are fostering strong partnerships with some of the sector’s biggest names including Sanofi and Exscientia and AstraZeneca and BenevolentAI.
However, you may be asking, why do I believe that 2023 specifically is the year that the true impact of AI and ML will be felt in the industry? Industry experts are predicting this year will bring the perfect storm. Through research, many iterations, trials, and in some cases errors, the AI and ML technology now on the market has been created with the specific nuances of the sector in mind.
The legal environment is also changing in the US with the FDA Modernization Act likely to be passed this year removing mandatory animal testing and opening up other pathways to prove safety which AI and ML technologies could fill.
While I am hopeful that this is the year the sector truly embraces these technologies, there are still obstacles to overcome. There is a persisting arms-length approach to integrating AI into traditional R&D efforts. In previous years, the technology may not have been quite ready; however, this is no longer the case. Organizations that do not utilize these technologies risk falling behind their competitors who can efficiently and safely bring drugs to market at a pace and cost never seen before.
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