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The Potential of Generative AI in the Digital Health Space

The use of AI in healthcare offers tremendous possibilities – as well as almost unprecedented levels of hype. Understanding where the real opportunities lie requires a clear view of the current state of the market and a grasp of the technologies that are on the horizon. It is still relatively early days in the journey of AI, but its influence is already being felt – the FDA has already approved over 500 medical devices that use AI in some form or another.

The digital health space feels like a natural home for AI, and in particular, generative AI. At Galen Growth, we’ve just completed a fascinating piece of research into the potential of GenAI in the market. As with the rest of the digital health space, AI in digital health is moving quickly and is attracting investment from a range of sources. Big tech players such as Microsoft, Amazon and Google are already dipping their toes into the water, and there are many much smaller players vying for their place in the arena. Over 39% of private digital health ventures across the globe use some form of AI.

Some of the most promising areas of impact for AI in digital health are outlined below.

  • Improving Diagnosis and Treatment: Generative AI can improve diagnosis and treatment in digital health by analysing large datasets and identifying patterns and trends that may not be apparent to human clinicians. This technology can reduce diagnostic errors and improve treatment outcomes by providing personalised recommendations for each patient. For example, a 2023 study found that an AI algorithm can accurately predict the likelihood of a patient developing Alzheimer’s disease up to three and a half years before diagnosis1 .
  • Increased Efficiency: Automation is a key benefit of AI in any sector, with its ability to take huge swathes of data and automate tasks that would take a human many hours. In clinical terms, this can mean taking clinical notes, patient reminds, billing forms and much more and using GenAI to analyse medical images and create standard analysis and documentation. The key here is to be clear that AI is an enabler, not a solution in its own right. Human-in-the-loop models ensure that clinicals remain in control.
  • Data Analysis and Drug Development: Generative AI can play a crucial role in data analysis and drug development. Large datasets used in clinical trials and drug development can be analysed using AI algorithms to identify patterns and trends that would not be visible to human teams. For example, in May this year, researchers in the US and Canada used machine learning algorithms to identify a new antibiotic useful in treating antibiotic-resistant bacteria2 .

The potential of generative AI in digital health is immense. However, we need to remain cautious as there are also challenges that we cannot overlook. Perhaps most importantly, there are ethical considerations around patient privacy and data security, particularly around sensitive data.

Then there are questions about regulatory compliance which will vary across different regions and which will constantly evolve in an attempt to keep up with the technology. The EU, UK and US are all looking at AI regulation and the news is full of concerns around ways in which AI could be abused across many industries, not least healthcare. Any organisation looking to build an AI capability into their digital healthcare solutions must strive to stay ahead of these issues if they are to take advantage of the enormous potential benefits to their business and to patients.

Find out more about Galen Growth and our digital health solution, HealthTech Alpha, here.

1 Predicting progression to Alzheimer’s disease with human hippocampal progenitors exposed to serum, Brain, Volume 146, Issue 5, May 2023
2 Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii, Nature Chemical Biology, May 2023

Julien de Salaberry

Founder & CEO


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