Navigating AI challenges in Healthcare and Life Sciences Industries

The deployment of artificial intelligence technologies in the healthcare and life sciences sectors has significantly increased during the last few years. The number of use cases for what was formerly a niche technology is absolutely remarkable, affecting every link in the value chain, and it is now a topic of conversation among business leaders on a daily basis.

AI has demonstrated its potential to revolutionize the industry by improving overall patient outcomes, streamlining operations, offering better insights and decision-making, lowering costs, and producing better commercial outcomes for the companies that learn how to use it. This has been demonstrated in a variety of settings, including smart manufacturing, connected labs, R&D, clinical operations, regulatory, and commercial topics.

AI is fundamentally changing how life sciences companies operate, but it also presents new challenges. This technology is based on mathematical equations and models, which demand a substantial amount of rich and diverse data.

One of the main challenges is getting access to this data. The unpleasant truth is that data is frequently siloed (dispersed across several systems and databases, even paper documents) and with insufficient quality.

Even if data were available, there would still be other aspects to take into account. The collection of historical data also reflects the previous methods and difficulties that our societies have faced. The data is collected over a specific time period, research and clinical trials data, medical records, etc. are often only representative of a small part of the population.

In light of these observations, another one has emerged: the possibility of bias in AI models. When the data used to train an AI model is not representative, bias might occur. An AI model, for instance, may not generate optimal outcomes for a more diverse population if it was trained on data from a certain demographic. This could result in discrepancies in care and lower outcomes for particular patient groups, perpetuating the existing disadvantages.

To identify and correct any potential biases and improve results, it is crucial to regularly monitor and assess AI models. Moreover, industry leaders should take these challenges as an opportunity to improve the existing business practices.

Imperfections are unavoidable in life, but they should motivate human ingenuity, creativity, and logic in order to increase societal outcomes.