AI Use Cases in Life Sciences Show Convergence, and Risk
The life sciences industry is in many ways converging with the field of artificial intelligence (AI), forecasting a future where AI use cases pervade our industry. Whether companies are developing therapeutics, vaccines, medical devices, or diagnostics, life sciences companies are actively exploring the incorporation of the technology into products, as well as critical operations throughout the product lifecycle, from discovery through commercialization.
This convergence is highly complex, and made even more so by the volatile state of the AI legal, policy and political debate around the world. Indeed, as we were finalizing this report, Donald Trump was re-elected and it is likely that the executive order on AI issued by President Biden will be withdrawn next year, reportedly to be replaced with a more innovation-friendly approach. As the industry knows from the past, however, governmental enforcement restraint does not necessarily follow such policy changes.
We surveyed leaders across the life sciences industry to learn how they’re using AI today, and how they plan to in the future.
Much of current AI activity in the life sciences industry is focused on discovery and research efficiency as well as optimization of operations to reduce time and costs. However, a major driver over time will be navigating the role of AI in the healthcare system of the near future, when AI tools and algorithms will likely drive a significant portion of patient care.
AI Will Transform Patient Diagnosis and Treatment
Increasingly, the patient journey will also be an AI journey — they will be diagnosed using imaging or digital tools developed with AI, treated using a sophisticated AI algorithm, enrolled in an AI-designed and monitored study, transitioned to the approved product utilizing an AI-customized treatment plan, and then be subject to monitoring via wearable sensors or implants incorporating AI. Each such healthcare development will have profound implications for those — including the life sciences industry — dedicated to serving these patients.
“Every new technology is a double-edged sword, but AI may be the sharpest blade yet.” —Deputy Attorney General Lisa Monaco, speaking in February 2024
Yet, as noted, these developments are not without risk. As life sciences companies evaluate and adopt predictive AI, generative AI, and machine learning (ML) models, these new technologies also open companies to a rapidly evolving AI regulatory and enforcement landscape that poses compliance challenges for the much-scrutinized life sciences industry.
The Future of AI in Healthcare, as Told by the People Shaping It
To understand how life sciences companies are addressing this emerging and disruptive technological convergence, Arnold & Porter surveyed 100 senior industry executives and department heads in key technology, leadership, and compliance roles. Respondents represented biopharmaceutical, digital health, medical device and diagnostic companies, among other types, based in North America and Europe; the majority reported between US$101 million and US$50 billion in 2023 gross revenue.
Our research found that while the adoption of AI is relatively new for many life sciences companies — three-quarters of our respondents only began implementation in the past two years — the pace of adoption is rapidly accelerating. Eighty-six percent of organizations are now in the process of implementing plans to deploy AI use cases within two years or less for research and development (R&D), manufacturing, marketing, regulatory, and other applications.
Life Sciences Must Weigh AI Risks Against Rewards
However, as is often the case with transformative technological change, governance measures appear to be lagging behind AI implementation. While many companies may be waiting to get more experience with piloting AI use cases, just 55% of respondents that are currently using AI have put AI policies and standard operating procedures in place
That process is likely to accelerate greatly in the coming year. While incredibly promising for patients and industry, the use cases for AI in life sciences can heighten compliance risks given the close link between some AI tools and clinical decisions, patient safety, and ethical treatment, as well as the use of huge volumes of often sensitive data. The cost and complexity of AI can also create major challenges by reducing transparency, accountability, and equity, and may heighten fraud and abuse risks in the deployment of AI tools and strategies with healthcare practitioners and patients.
Thus, in parallel with the patient journey shaped by AI, the industry will need to continually assess and control its activities to ensure that the risks from the incorporation of AI into the business do not produce investigations or litigation, delay important transactions, or embed financial vulnerabilities that could severely damage the enterprise in the years to come.
Our full AI and Life Sciences report examines key industry benchmarks for AI implementation across the product lifecycle, including detailed insights on how companies are progressing in AI adoption, governance, compliance, and risk mitigation strategies. In the last section of this report, we also provide Arnold & Porter resources on some of the key risk considerations that companies must address in parallel with this fundamental technological and healthcare evolution.
