How is AI transforming the life sciences industry?

 
Stakeholders are grappling with the numerous opportunities and risks presented by the convergence of artificial intelligence and tools designed for the development of novel therapeutics and the delivery of health care.

Drug discovery and clinical trials

AI is considered by many as a groundbreaking tool for use in drug discovery and precision medicine. The clinical translation of AI-discovered novel drug-target interactions is rapidly accelerating, with tangible evidence of success emerging across multiple therapeutic areas. Drug developers are using models trained on large data sets to not only identify biological targets that cause disease but to also select potential drug candidates that may interact with those targets. As of 2024, over 70 investigational new drug applications submitted to the U.S. FDA involve artificial intelligence or machine learning in some capacity¹. In Canada, the federal government has taken several steps to further streamline the use of AI in this area, including a $49 million investment through the Strategic Innovation Fund for the Conscience Open Science Drug Discovery Network².

Life sciences companies are also using AI to streamline clinical trial activities—including monitoring study subjects in real-time and identifying patient populations most likely to benefit from an investigational product. With this technology, reviewers may identify patterns that might otherwise be overlooked by human researchers, although the need to reduce bias in data sets must be carefully managed to not perpetuate health disparities.

AI-powered support tools

AI systems are being rapidly developed in the form of clinical decision support tools to assist clinicians in making more efficient, evidence-based decisions. Chatbots can be used to provide ongoing patient support, triage, and follow-up, so that human providers can focus their limited resources on other matters. Further, AI driven electronic medical records can be used to reduce administrative burdens placed on physicians for record keeping purposes.

Globally, investors have turned to provider-centric workflow tools such as “AI scribe”, with companies having raised over U.S. $1.6 billion to date this year, compared to $390 million in 2023³. Traditional health technology companies are also entering the AI-powered space. For example, Epic Systems (a leading electronic medical records provider in the U.S.) recently announced the launch of its own AI scribe tool. 

Of course, such tools raise questions of legal liability; in particular, around who is responsible in the event that the AI tool causes harm: the developer, the health care provide or the health care institution. Indemnity, liability cap and insurance provisions should be carefully considered when contracting in this space.

Recent advancements in AI include the use of synthetic patient data sets to manage the privacy related risks of training AI tools for the health care sector. These data sets are essentially artificially generated health records designed to mimic the statistical properties of real patient data without containing any actual patient information. They can be used for AI model training, drug development and research simulations; although many critics question whether such synthetic data can be generated in sufficient quality to mimic large, robust real-world data.

Medical devices

AI models are being used increasingly in medical imaging applications to detect abnormalities or patterns used for diagnosis in X-rays, MRIs, or pathology slides with high precision. Many of these AI-driven technologies are regulated as medical devices in Canada and are subject to heightened pre-market requirements. In 2025, Health Canada released its pre-market guidance for machine learning-enabled medical devices, providing much needed regulatory guidance to developers of Canadian AI based medical devices, on topics including good machine learning, training and validation data selection and predetermined change control plans.  More direction from Health Canda is expected in the near future due to the pace of developments in the AI driven medical device sector.

Investment in the space

AI is creating exciting investment opportunities across a number of healthcare segments. U.S. AI-focused startups and scaleups secured 60% of all digital health funding in the first quarter of 2025, raising $3.2 billion and representing a dramatic increase from 41% in 2024⁴. AI investment is particularly pronounced in the biopharma sector, which experienced a 300% increase in AI investment since 2023—with over $5 billion directed toward biopharma AI companies in 2024 alone, surpassing 2021 totals by nearly $2 billion⁵. Other healthcare domains in which investor appetite remains strong include administrative automation (capturing 42% of all deals in 2025, a 26% increase from 2019); while clinical decision support tools represent 32% of deal activity. Within clinical applications, patient diagnostics account for 52% of total investment⁶.