Experts’ Disagreement with Medical Literature Leads to Exclusion

Peer-reviewed literature can be a powerful tool in attacking an opposing expert’s opinions.  A solid, on-point article can do more than merely satisfy several of the so-called Daubert factors for assessing reliability – by showing a court that others in a challenged expert’s field disagree with his or her opinions, literature can remove any expert “aura” that might discourage a lay judge from discharging his or her duty as a gatekeeper.  Presenting literature that directly undermines the expert’s opinion can make the difference between winning and losing a motion to exclude, especially where the expert’s opinion is not supported by other literature accepted in the field.

A recent example is U.G. v. United States, 2022 WL 7426212 (S.D.N.Y. Oct. 13, 2022), a medical malpractice action under the Federal Tort Claims Act in which plaintiff suffered a shoulder injury during his birth and was later diagnosed with permanent Erb’s palsy, or brachial plexus injury.  He alleged that the obstetrician caused the injury by using excessive force on his head and shoulders during delivery.  In support of his claims, he offered two causation experts – an obstetrician/gynecologist and a pediatric neurologist– both of whom claimed the “totality of the circumstances” ruled out several possible alternate causes and thus showed that the defendant caused the injury.

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Responsible AI: Managing Risk in an Evolving Regulatory and Legal Landscape

Artificial Intelligence (“AI”) is driving innovation across industries and is playing an increasing role in everyday life. AI (and, more broadly, algorithms) is being used in diagnostics, enabled medical devices, device manufacturing, drug discovery and manufacturing, industrial manufacturing, smart home and wearable devices, and vehicles. AI has the potential to benefit society in a number of ways, including by boosting efficiency, providing invaluable insights, and informing decisions. But to borrow from the likes of Winston Churchill (or Spider-man), with great AI capabilities comes great responsibility.

Many of the risks and benefits of AI systems are unique in comparison to other technologies. Regulators, governmental agencies, and consumer advocates are keenly focused on addressing unintended consequences that may result from the development and use of algorithms and AI. These issues, like the technology, are complex, and policymakers, stakeholders, and regulators are grappling with how best to address them.

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“Alternative Avenues” Argument in Failure-to-Warn Cases Adding an Additional Wrinkle to Medical Device Litigation

A developing line of cases across the nation may have large implications for medical device manufacturers defending against failure-to-warn claims.  While a treating physician’s failure to read or rely on the manufacturer’s warnings has historically been fatal to a failure-to-warn claim in many jurisdictions (at least those without a “read and heed” presumption), plaintiffs have tried novel “alternative avenues” arguments to make summary disposition of the claim more difficult.

There are two theories under which a failure-to-warn claim may be brought in the products liability context:  a manufacturer with a duty to warn may breach its duty by either (1) failing to provide an adequate warning of the product’s potential risks (the “content theory”) or (2) failing to adequately communicate the warning to the ultimate consumer (the “communication theory”).  Plaintiffs have traditionally pursued failure-to-warn claims in prescription medical device cases under the content theory, with most courts holding that a treating physician’s failure to read or rely on the manufacturer’s warnings in the product’s instruction for use (“IFU”) is fatal to the claim.  See, e.g., Foster v. Ethicon, Inc., 2021 WL 1169473, at *7 (D.S.D. Mar. 26, 2021).

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European Commission Updates Liability Rules to the Digital Age

The European Commission adopted two proposals that adapt liability rules to the digital age, circular economy and the impact of global value chains. These proposals are related to the Revised Product Liability Directive, fit for the green and digital transition and global value chains, and the AI Liability Directive to provide easier access to redress.

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Ipse Dixit – It’s Not Just for Analytical Gaps Anymore

There are few legal phrases more fun to say than “ipse dixit.” The phrase is most commonly used in motions to exclude experts who base their opinions on nothing more than their own say so.  As the Court noted in General Electric Co. v. Joiner, 522 U.S. 136 (1997), an ipse dixit – Latin for “he said it himself” – leaves an impermissible “analytical gap” between the expert’s opinion and the facts on which it is based.  But ipse dixit arguments can and should stretch beyond just the “basis” part of the expert argument. Courts should also exclude experts who provide unsupported and self-serving testimony to suggest that their method is accepted generally in the community.

That is precisely what happened in Knepfle v. J-Tech Corporation, 2022 WL 4232598, — F.4th — (11th Cir. 2022).  Plaintiff was injured in a motor vehicle accident when she ran her motorcycle into the side of a vehicle that had turned in front of her, causing her to be thrown from the motorcycle.  Although the helmet she was wearing protected her head during the initial impact with the other vehicle, she alleged that it came off and failed to protect her head when it struck the pavement.  She brought product liability claims against multiple defendants in the manufacturing and distributing chain of the helmet.

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Smart Contracts — A Few Tips to Avoid Being Outsmarted

Proponents of digital innovations such as blockchain, the Internet of Things (IoT) and smart devices have hailed the introduction of such technology as the Fourth Industrial Revolution. When used together, they may create self-executing “smart contracts” for a variety of transactions. Smart contracts do not need to rely on IoT devices, but when they do, these devices are critical to the system, most importantly because they collect and transfer the transaction-related data that triggers the execution of the contracts. But how is that data verified, and what happens if the IoT devices are wrong?

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