Well into 2024, artificial intelligence continues to be the number one topic of discussion across industries and in media. As technology improves, more and more life sciences companies are beginning to introduce or develop their own AI platforms and advanced technology systems for both product creation and business management.
However, while AI and advanced technology offer a pathway to greater innovation, they also open another door for bad actors to target life sciences companies through data breaches, ransomware, and data infiltration. Regulations around cybersecurity and protections for AI continue to evolve quickly in response to these new developing technologies. Nevertheless, the Department of Justice has made it clear that companies will need to show that compliance programs address and mitigate risks related to the use of AI if and when breaches do arise.
At Buchanan Ingersoll & Rooney's 2024 New Jersey Life Sciences Summit, our team of attorneys and government relations professionals discussed trends in AI and advanced technology adoption in the life sciences industry, what this means from a safety and security perspective, and the steps companies can take today to navigate risks, protect intellectual property, and stay compliant with changing regulations.
Here are five ways AI and advanced technology are impacting life sciences companies today.
1. Evolving FDA Regulatory Guidance
There is a lot of excitement around AI adoption in life sciences today, but it's still in the early stages of implementation. The Food and Drug Administration has been keeping up with the latest developments in AI and has been issuing guidance on the use of AI for the last few years. One area the FDA has focused on is the use of AI in developing diagnostic devices, as well as in assisting with drug development. For diagnostic AI devices, FDA requires that companies be able to explain how the algorithm or AI model works, how it's being used, and how it's being secured.
Right now, the FDA is showing greater concern over the use of AI and machine learning in testing and less so in the drug discovery phase since FDA does not regulate how drug manufacturers discover or investigate new drugs to test in clinical trials. The FDA is also working with other agencies on guidelines for the security of the data used in AI models. As guidelines continue to be updated, transparency and explainability will be key for life sciences companies adopting AI for drug development.
2. Use in Clinical Trials
AI offers great potential for improving clinical trial results, patient recruitment, and data analysis. However, for life sciences companies implementing AI in their clinical trial processes, there are a few key considerations when it comes to data security and usage.
AI platforms used in clinical trials create another target for bad actors to breach data, which could compromise the trial and its results. Companies using AI for clinical trials will need to ensure they plan and prepare for possible data breaches and have sufficient prevention measures in place to protect study subject data. Furthermore, they need to make sure that the AI that they use does not gather information that it can subsequently provide to other users outside of the company. Trial sponsors must also carefully consider how they explain the use of advanced technology, such as AI, in the informed consent document so study subjects understand how their data may be used. Finally, companies need to consider how they can leverage the use of AI to assess clinical trial data – for example, to teach AI models for future drug discovery or to inform AI models to develop a second generation of a particular product.
3. Product Liability Risks
The introduction of AI into product development and manufacturing brings a host of new risks for life sciences companies when it comes to product liability. Key areas include inherent bias and discrimination in the AI models that companies are using, lack of transparency and explainability in terms of how the algorithm works, security concerns regarding potential breaches and resulting liability consequences and, finally, inconsistent regulatory approaches.
While this technology is rapidly evolving, the legal precedent is very limited in terms of product liability, and regulations and laws at both the federal and state levels are still catching up. With some states like California ahead on the regulation front and others taking no action at all, it can create challenges for companies operating across the country or across the globe. Life sciences companies will need to ensure compliance with any relevant federal, state, or local laws and be prepared to defend their AI models with transparency and explainability should a lawsuit arise.
4. Patent & Trade Secret Protection Concerns
AI offers innovative potential in developing technology autonomously but can make securing patents more challenging as inventorship still requires a human inventor. In its guidance, the U.S. Patent and Trademark Office shows that it is looking for a signifiant human contribution to each claim in an AI-related invention. Life sciences companies will need to show significant contributions from human inventors when applying for a patent. If that's not possible, this brings up an important analysis life sciences companies will need to conduct to determine if an invention is patentable or, better, kept on the shelf as a protected trade secret.
On the other side, the USPTO is starting to use AI and advanced technology tools to help patent examiners complete patent searches and identify prior materials. This could benefit life sciences companies by strengthening patents and potentially helping to limit the number of patents overturned in litigation.
5. Talent Management Bias Prevention
While AI is being used in the research and development side of life sciences companies, there are also emerging use cases in hiring, recruiting, and talent management. AI can be leveraged to streamline many aspects of the hiring and recruiting process, including creating job postings, communicating with candidates, and tracking candidate progress through the hiring cycle. For onboarding and talent management, advanced technologies can be used for training modules, communicating with employees concerning benefits offerings, collecting feedback on employee performance, performance monitoring, and much more. It's also being used by human resources departments to streamline accommodation requests, minimize language barriers, and audit HR processes and pay data.
While there's a lot of opportunity, advanced technology tools also introduce risks in HR and talent management, most notably the risk of bias. Several lawsuits have already been brought against companies using AI for applicant screening, alleging exclusion for discriminatory reasons. There are also data security concerns that come with using AI tools in talent management. Life sciences companies looking to implement these tools will need to take appropriate workforce training and compliance measures.
Navigating AI and Advanced Technology Implementation
While AI and advanced technologies offer a breadth of opportunity in the life sciences space for innovation and acceleration, there can be resistance and hesitancy from companies or employees to embrace these tools. It's critical to follow a digital literacy approach that exposes employees to new technologies and provides training on how to use them, how to keep them secure, and how to protect data. It's one of the most important ways life sciences companies can successfully navigate the adoption and implementation of AI and advanced technology.
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