When should pharma execs actually care about AI?

Artificial intelligence (AI) has recently garnered a lot of attention, and it is transforming the way pharma execs use their data. tech emergency, an AI market research company, recently revealed that interest in pharma-related AI articles has gone up by more than 10 times in the last year. What’s more, over 50% of healthcare industry executives expect an increased adoption of AI on a wide scale by 2025.

It seems like every company is either calling itself an AI company or looking to collaborate with companies that label themselves as such. This mad rush for AI can be distracting, and pharma execs can be overeager to integrate AI without considering what problems it actually solves.

AI is only valuable insofar as it solves real-life problems. Pharma execs should not be asking, “How can we integrate AI into our company?” Instead, they should be asking, “What problems do we need to solve, and is AI the right tool to solve those problems?”

Separating The Hype From Reality

AI has sparked phenomenal progress in advanced algorithms, which have led to breakthroughs across several industries — and this undeniable success makes the hype defensible.

The challenge for pharma execs is whether they can separate the hype from reality. If pharma companies cannot acquire high-quality data for AI algorithms to work with, the results produced by AI will be unfruitful. David Lareau, the CEO of Medicomp Systems, said that “These much hyped ‘innovations’ cannot learn to be effective and accurate from poor quality data, which is a major challenge in healthcare, especially in the clinical realm where data is complex and often unstructured.”

Another reason why pharma execs should care less about AI in and of itself is that there is a shortage of AI experts. Only a few people (probably fewer than 10,000 professionals globally) are experts in AI, and even fewer people are experienced in pharma-related AI.

Due to this scarcity of talent, pharma execs should instead seek to collaborate with other pharma and AI-driven companies to solve major problems. Take, for instance, the 2017 research partnership between AstraZeneca, a major pharma player, and Berg Health, a Boston-based biopharma company with a unique AI-based platform. The collaboration between the two companies is a good example of pharma leveraging AI expertise to solve a real-life problem — in this case, Parkinson’s disease.

However, pharma companies should take care not to outsource their AI-driven analytics entirely. Instead, they should seek to partner with companies whose products can elevate and empower their own researchers to achieve greater heights of innovation.

The Big Problem That AI Can Solve In Pharma Right Now

AI has the power to take complex, disparate data sets, or unstructured data, and structure it in order to illuminate connections and relationships between data. For example, AI can find meaningful relationships between unstructured data as diverse as a medical scan from Florida, a doctor’s note from New Delhi and a research paper from Brussels.

Although many industries successfully use AI to make meaningful connections with their data, the pharma industry introduces an additional layer of complexity by using biological data. Data sets in pharma typically include a wide array of unstructured data extracted from various sources, including genomics, proteomics, metabolomics and protein interactions. The amount of variation within each source is incredibly high and poses a challenge for AI to highlight relationships and connections.

However, pharma companies can yield optimized results if scientists keep adopting efficient AI algorithms and automated processes. By using AI to structure and uncover meaningful relationships in disparate biological data, pharma execs can empower researchers to spend less time on lower-level cognitive tasks, like organizing and manually structuring data. In turn, researchers will have more cognitive bandwidth and time for creativity and “aha” moments.

In addition, by using AI to find connections and relationships, researchers will be able to answer questions they did not even know they were asking. A researcher can look at a robust, rich network analysis of connections and relationships between data points and see insights that they did not know they were looking for.

Increasing investments by major pharma players provide evidence that the use of AI applications will continue to explode within the pharma industry. However, the onus is on pharma execs to set themselves apart and rise above the competition by focusing on the AI tools that provide solutions to real problems — not just on AI for its own sake.

The original article was published on Forbes

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