AI in Life Sciences: Ten Legal Considerations and Risks of AI Use in Drug Discovery and Development

Over the past several years, the biopharmaceutical industry has embraced artificial intelligence and machine learning (AI/ML) in near lockstep with the pace of AI/ML innovations. Today, industry leaders are using AI/ML to, among other things: discover and assess biological pathways, target chemical structures and sequences; design proteins; model pre-clinical and clinical trials; recruit and screen potential patient populations; evaluate clinical trial results and biomarker data; prepare regulatory filings; and manage supply chains. Deployment of new AI/ML models promises extraordinary advances in pharmaceutical development. However, as with any technological and scientific advances, the use of AI/ML also poses substantial legal risks that life sciences companies need to consider and proactively manage. 

This client alert outlines 10 core legal considerations and risks when deploying AI/ML across a broad spectrum of the life sciences ecosystem. Companies in this space should proactively identify and address these potential exposures where relevant. These risks impact a multitude of stakeholders: emerging companies that build, deploy, and are consumers of AI/ML platforms; venture investors, acquirers, and collaborators that conduct deal diligence to minimize transactional exposure; and established biopharmaceutical companies that must govern AI/ML use across the enterprise and meet disclosure obligations. Each of these stakeholders must manage these uses and potential exposure where relevant. 

1. Intellectual Property 

Use of AI/ML in the development of intellectual property (IP) and technology platforms raises critical issues for life science companies and their investors. They fall into two broad categories. 

First, inventions discovered or developed by AI/ML systems may not, in some instances, be patentable because patent law requires that an inventor be a human being. U.S. federal courts have held in IP cases involving a nonhuman creator that a human being is required for inventorship purposes. In November 2025, the U.S. Patent and Trademark Office (USPTO) issued revised inventorship guidance consistent with those holdings, confirming that the same standards apply to AI/ML as for other research tools. Specifically, while AI/ML can assist in developing an invention, the inventive concept must originate with a human. As a result, only a human can be named as an inventor on a patent application. Patent law systems and court cases in countries outside the United States have adopted similar patentability standards and ruled that AI cannot be recognized as an inventor.  

Second, AI/ML tools that train using confidential data may jeopardize IP protections, including trade secret protection. More specifically, when a party inputs confidential information (whether as documents, prompts, or otherwise) into publicly available AI models, that party risks losing legal protections applicable to such information, as the act of disclosure may cause it to enter the public domain or otherwise be accessible to third parties. 

2. Reliability of AI-Generated Evidence 

Because AI models are inherently probabilistic and can generate “hallucinations,” including false biochemical data or references, AI-generated evidence in drug development raises substantial reliability concerns. Models often train on incomplete historical datasets, which can skew predictions. Given how AI/ML systems are trained, it is difficult for scientists to trace how an algorithm reached a specific conclusion. Furthermore, even small shifts in input data can lead to drastically different outputs, compromising experimental reproducibility. Finally, malicious actors can exploit security vulnerabilities to manipulate training data and intentionally distort non-clinical and clinical trial results. Humans with relevant experience will want to ensure the accuracy and completeness of data input and validate output independently of the AI/ML systems. 

3. Reliability and Accuracy 

When AI/ML tools used in drug development or clinical trial operations are trained on datasets that do not reflect the patient populations for which a product is intended, the results can cause performance to vary across demographic and clinical subgroups. In January 2026, the Food and Drug Administration (FDA) released guidance, “Guiding Principles of Good AI Practice in Drug Development.” Notably, the guidance highlights the importance of considering how to effectively manage technologies during drug development to create reliable and generalizable outputs. In August 2026, FDA published a discussion paper, “Considerations for the Regulation of Generative AI-Enabled Medical Devices: Discussion Paper and Request for Feedback,” seeking input on a potential regulatory framework for AI-enabled medical devices (see Crowell alert). As FDA’s AI initiatives evolve, companies should expect additional guidance from FDA on how to govern AI-enabled processes in the drug development area to ensure that testing, monitoring, and governance practices are properly used to evaluate and mitigate these accuracy and reliability concerns. 

4. Product Liability 

When AI-assisted drug development contributes to adverse outcomes, liability attribution can be complex, and the caselaw is evolving rapidly. Companies unable to explain model outputs or document validation decisions may face significant litigation exposure. Claims alleging AI-related harms in products liability, consumer protection, deceptive trade practices, and related torts are multiplying. Europe has updated its EU Product Liability Directive, which Member States must transpose by December 9, 2026, to bring AI/ML systems within the definition of “product,” and which authorizes courts to presume defectiveness where a manufacturer fails to disclose AI/ML training and validation data. 

5. Bulk Data Transfer and Export Controls 

The U.S. government has launched strict new security rules, including bulk data transfer rules, to protect Americans’ medical and genetic data from foreign adversaries. These regulations tightly control how health care and life sciences companies can share health data globally, accept foreign money, and trade technology. The goal is to stop certain nations from using private medical information to build surveillance systems, design biological weapons, or train military AI. While bulk transfer regulations include a safe harbor for such transfers in connection with clinical trials, these do not apply to various secondary uses or AI training. Violating these rules can lead to significant fines or imprisonment, making data security a top priority for global health companies. 

6. Re-Identified Data 

The integration of AI/ML in drug development introduces a significant regulatory risk concerning data privacy: data re-identification. AI algorithms train on datasets, often including patient health records and genomic data that has been stripped of direct identifiers to comply with privacy laws. However, advanced AI techniques can analyze patterns, cross-reference disparate databases, and combine apparently anonymous data points to reconstruct individual identities. This capability undermines traditional de-identification standards, and their use may violate privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) in Europe. Life sciences companies should weigh the regulatory scrutiny and liabilities and seek to determine whether the AI models they deploy may inadvertently expose sensitive patient information or breach data processing consents. 

7. Scope of Informed Consent 

Many existing informed consent forms (ICF) and biobank permissions were written to cover only specific research protocols. As a result, they may not give companies the right to use patient blood, tissue, organ, or genomic data to train AI/ML models or run AI-driven analyses. Even if the original ICF contemplates secondary uses of samples and data generally, it may not be broad enough to cover specific uses of AI. When proper authorization is missing, the companies sponsoring those studies may need to go back to participants and obtain new or broader consent under applicable laws and regulations, which can cause significant delays, increase expenses, trigger pushback from institutional review boards, or force the exclusion of valuable data. Evolving state health-data laws increasingly demand specific, affirmative consent for each intended use of protected health information or other personal health data. Older consent forms are unlikely to meet that standard, exposing companies to enforcement actions and litigation. 

8. Antitrust and Data Pooling 

Data-pooling consortia and AI-enabled co-development can speed up drug discovery and development but can also create antitrust risk. Regulators focus on whether competitor collaboration and information sharing could reduce competition, even if companies claim they are doing so for a “scientific” purpose. The Federal Trade Commission (FTC), U.S. Department of Justice (DOJ), and European Commission currently diverge on their public guidance related to data aggregation, AI-driven coordination and market power, and related deals. However, regulatory positions in this area remain in flux, necessitating early focus on guardrails and diligence on consortium terms and AI/data acquisitions. 

9. AI-Washing 

Life sciences companies that overstate the capabilities or integration of AI/ML in their platforms, or fail to disclose the associated risks of AI/ML use, face legal exposure in both public and private securities markets. The Securities and Exchange Commission (SEC) has pursued enforcement actions against companies for AI-related misrepresentations in securities filings and investor communications, and similar theories have been advanced in private litigation. For emerging companies raising capital or seeking a commercialization partner or acquirer, representations about AI/ML capabilities in pitch materials, term sheets, and offering documents create risks of breach of contract and damages claims. 

10. Governance 

As AI adoption increases, directors and executives must consider the related enterprise risks as part of their duties of oversight. Boards and executives may face breach of fiduciary duty or other liability if they fail to develop appropriate compliance controls or the reporting systems required to effectively oversee AI adoption and deployment. 

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The rapid adoption of AI/ML in drug discovery and development offers great potential but introduces a wide and varied range of interconnected legal implications and potential liabilities. Avoiding and managing these pitfalls requires coordinated counsel from an interdisciplinary legal team. Failure in any one of these areas can cascade into problems across several others. Life sciences companies and investors that proactively identify and address these risks will be better positioned to protect their assets and withstand scrutiny during financing, regulatory review, and strategic transactions. Future alerts in this series will examine these risk areas in greater detail and discuss the advantages and drawbacks of potential strategies for managing them.