We’re about to enter the Fifth Industrial Revolution where artificial intelligence (AI) and humans acting in synergy create an outsized impact on the world.

The discourse around AI has for years been focused on its potential to revolutionise the way we live and work. In digital health, it’s bearing fruit and AI now has a broad range of applications including patient and physician engagement, and education and clinical trial design.

However, there are challenges such as the inherent biases in the technology, resulting from the fact that its parameters and governance are still based on human decisions, and limitations in current understandings of its use. While AI tools will need to be developed in an ethical and responsible manner, their potential benefits make any such investment worthwhile.

AI has the potential to be as transformative for society, and therefore healthcare, as electricity and the steam engine were to industrialisation.

AI is often mischaracterised and people use the term loosely without understanding its different branches and capabilities. AI in short is a collection of emerging areas of innovation that are essentially computer models built from our understanding of human intelligence to approximate, automate, enhance and optimise the ability of machines to do human thinking.

It’s not a monolithic tool that users can simply apply without thought. There are different types of AI, such as natural language processing, machine learning, deep learning and machine vision, and they are used depending on the specific application.

AI’s most promising use case in clinical trials is within predictive analytics and analysing data from previous trials to identify factors that make certain trials more successful than others. By understanding these factors, researchers can focus on interventions that can improve trial outcomes. Virtual versions of trials, or ‘digital twins’, can be created using AI to simulate different scenarios and test the potential impact of changes before implementing them in a real trial. This helps to minimise the risk of costly mistakes and optimise trial outcomes.

In short, AI enables researchers to improve the efficiency and effectiveness of clinical trials, which can ultimately lead to better patient outcomes.

In the field of digital Medical Affairs, AI tools can adapt to various tasks, such as standing up a scientific platform or using generative AI to write content.

Machine learning, deep learning and natural language processing help democratise the analysis of data to create insights on how best to position a product in the market. AI tools can ingest the full corpus of relevant content and analyse it in minutes, something which would have taken humans months or years to do, delivering data on white spaces and competitors.

This allows teams to speed up their ‘time to decision’ and make more informed choices.

Using machine learning and natural language processing techniques, companies can also ingest and analyse large volumes of medical records and other data, even incorporating social media posts, to identify patterns and make predictions about patient outcomes. This can help healthcare providers target treatments and interventions more effectively and efficiently, potentially saving lives and reducing healthcare costs. It’s a great example of how AI can be used to augment human decision-making and improve healthcare outcomes.

As with all emerging technologies, there is cause for concern and for optimism. It is important to evaluate the potential promises and pitfalls of AI technologies.

Pharma and biotech businesses and their strategic partners should carefully evaluate the potential benefits and risks of AI technologies and experiment with actual applications to gain insights and determine how the technologies fit within their ecosystem.