AI Platforms Can Help Product Liability Litigation Move Forward

Before new Rule 16.1 of the Federal Rules of Civil Procedure, thousands of plaintiffs too often sat for long periods in products liability multidistrict litigation with little known about their claims.

But some MDL parties and judges are now tackling this challenge with artificial intelligence, data analytics, and automation. Although we don’t endorse any specific platform, we encourage litigants to use such examples in appropriate cases to advocate for similar approaches through the early report and conferences expected by Rule 16.1.

Although Rule 16.1 doesn’t expressly mention these developing technologies, such tools fit within the rule’s required discussion topics, including the requirement to discuss “how and when the parties will exchange information about the factual bases for their claims and defenses”; “discovery, including any difficult issues that may arise”; and “whether the court should consider any measures to facilitate resolving some or all actions before the court.”

In this regard, technological solutions can help MDL courts:

• Ensure that plaintiffs’ claims meet basic requirements (such as having proof of product use and injury)
• Gather, organize, and assess case inventory details and early plaintiff-specific discovery
• Resolve pleading and disclosure deficiencies

Litigants can use these tools to support a data-driven MDL that helps shape important decisions on which cases to work up through discovery pools, selection of potential bellwether trials, whether to implement Lone Pine or similar orders, and other resolution opportunities.

In the Depo-Provera MDL, which commenced last year, the parties and the MDL court implemented an AI-enabled platform. The platform is designed and maintained by the designated MDL data administrator, an outside company appointed under Pretrial Order No. 6 that isn’t otherwise related to the litigation.

The MDL court ordered the parties to use this platform for complaint service/tracking and pleading deficiencies analysis, completion of proof of use/injury questionnaires (along with uploads of “threshold documentary proof”) and related deficiencies analysis, and other documents that provide information about plaintiffs’ claims. At a May 30, 2025 court conference, the MDL data administrator stated that it programmed its “own sort of automated review or AI system to digest the complaints, to digest the proof of use, [and] to digest the documents that come in on diagnosis.”

This type of early analysis across all plaintiffs unfortunately hasn’t been the standard across all products liability MDLs. The orders and hearings from the Depo-Provera MDL show that a properly functioning Rule 16.1 process requires ongoing collaboration among counsel, the court, and any MDL data administrator.

For example, Depo-Provera defendants can raise deficiencies the AI platform and MDL data administrator don’t catch. The failure to correct deficiencies can lead to case dismissal including dismissal with prejudice. The MDL court commented at the May 30 conference that “this has been the smoothest deficiency process I’ve ever seen.”

As with other AI platforms, the one used in Depo-Provera requires human training, quality control, and general oversight. For instance, the MDL data administrator noted at the May 30 conference that the system had difficulty understanding that a health-care provider’s reference in medical records to ruling out a diagnosis didn’t mean the claimant had the diagnosis. This led to further training of the AI and quality control team.

And at a July 11, 2025 court conference, the MDL data administrator reported the AI technology performing at 84% to 85% accuracy. That is a high success rate, but it shows AI can’t substitute for attorney involvement and review. Human quality control avoids AI errors determining final outcomes.

Parties to the Suboxone products liability litigation used a different company’s platform that describes itself as built with automated processes and interactive analytics that reduce manual tasks and inform important decisions on bellwether trial selection and other processes.

For example, under Case Management Order No. 12, the Suboxone MDL court required use of the platform for completion of a plaintiff census form, including to gather data on use, damages, and injury issues and for uploading documentary proof of use and injury (subject to potential case dismissals for non-compliance).

The court also used data generated from the information plaintiffs entered into the platform to help identify and address dual representation of plaintiffs. In a Jan. 14, 2025 court conference, the parties reported eliminating 278 duplicate entries with the help of this platform. In the GLP-1 receptor agonists MDL, in CMO No. 12, the parties are using this same platform to complete a more detailed plaintiff fact sheet. Beyond identifying individual cases for early dismissals, aggregate data analytics and statistics efficiently generated from such platforms inform MDL decisions on trial workup cases and more.

These and other cases demonstrate that AI, data analytics, and automated technologies can provide efficient ways to assess large amounts of data and move cases forward—if implemented correctly and appropriately supervised.

This is still a new application of AI technology, so it’s imperative to review the pros and cons of this strategy with legal counsel as it pertains to each specific litigation. If deemed beneficial, parties should discuss these opportunities early and proactively use MDL case examples when advocating under Rule 16.1 for an early technology-enabled MDL census and/or plaintiff fact sheet program, pleading/census/plaintiff fact sheet deficiency analysis, and other AI-enabled management strategies.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

James Sullivan is a partner at Hollingsworth focused on complex civil litigation, including pharmaceutical and chemical defense, products liability and toxic torts, and engineering and construction litigation.

Abigail Cahn-Gambino is an associate at Hollingsworth, practicing complex litigation defense in pharmaceutical, medical device, products liability, and toxic tort matters.

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