ARTIFICIAL INTELLIGENCEINVITED PERSPECTIVEHow Much Can Potential Jurors Tell Us About Liability for
Medical Artificial Intelligence?
W. Nicholson Price II1,2, Sara Gerke2,3, and I. Glenn Cohen2,3
1University of Michigan Law School, Ann Arbor, Michigan; 2Project on Precision Medicine, Artificial Intelligence, and theLaw, Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics at Harvard Law School, Harvard University,Cambridge, Massachusetts; and 3Harvard Law School, Harvard University, Cambridge, Massachusetts

See the associated article on page 17.

A

rtificial intelligence (AI) is rapidly entering medical practice,whether for risk prediction, diagnosis, or treatment recommendation.
But a persistent question keeps arising: What happens when things
go wrong? When patients are injured, and AI was involved, who will
be liable and how? Liability is likely to influence the behavior ofphysicians who decide whether to follow AI advice, hospitals that
implement AI tools for physician use, and developers who create
those tools in the first place. If physicians are shielded from liability(typically medical malpractice liability) when they use AI tools,
even if patient injury results, they are more likely to rely on thesetools, even if the AI recommendations are counterintuitive. On theother hand, if physicians face liability from deviating from standardpractice, whether an AI recommends something different or not, theadoption of AI is likely to be slower, and counterintuitive rejections—even correct ones—are likely to be rejected. In this issue of The
Journal of Nuclear Medicine, Tobia et al. (1) offer an important
empiric look at this question, which has significant implications asto whether and when AI will come into clinical use.
In 2019, we offered a set of possibilities to explore physician liability
for the use of AI to make treatment decisions, concluding that underexisting law the safest path for physicians was to use AI as a confirmatory tool but ultimately to stay within the existing standard of carewhen AI recommendations stray from that standard (Fig. 1, columns
1–6) (2). The study of Tobia et al. takes the scenarios we suggested andasks a simple but important question: How would potential jurors (here,2,000 individuals in an online vignette study) view our scenarios interms of whether the physician’s actions were reasonable when a patient
was harmed? Their results, partially shown in abbreviated form in
Figure 1 (column 7), offer an important additional dimension to ourdoctrinal analysis. Essentially, potential jurors indicated that following the standard of care could result in no liability for physicians—
but independently, so could following the advice of the AI system. If
Received Oct. 14, 2020; revision accepted Oct. 30, 2020.
For correspondence or reprints contact: W. Nicholson Price II, University ofMichigan Law School, 921 Legal Research, 801 Monroe St., Ann Arbor, MI48109.
E-mail: wnp@umich.edu
Published online Nov. 6, 2020.
COPYRIGHT © 2021 by the Society of Nuclear Medicine and Molecular Imaging.
DOI: 10.2967/jnumed.120.257196

a physician followed an AI recommendation to deviate from the normal
standard of care, and the AI was actually incorrect, with patient injury
resulting (Fig. 1, scenario 4), potential jurors were fairly likely to findthat decision reasonable. In fact, potential patients seemed to find following the AI’s recommendations more important, from a reasonableness perspective, than following the preexisting standard of care.
IMPLICATIONS FOR LIABILITY IN MEDICAL AI

These results suggest that, at least with respect to potential
jurors and lay understanding, the use of AI might be closer to thestandard of care than we might think. Following the advice of AI
already reduces the risk of liability for injury that results fromdeviations from the standard of care. The contrary is not yet true,
though: deviating from nonstandard AI recommendations is not
yet viewed as subjecting a physician to liability on its own. Thispattern, if it holds, should help physicians feel more at ease using
AI to help them make decisions—and not just relying on AI toconfirm what they already think. Developers and hospitals might
similarly be more willing to create and implement AI tools in practice.
However, the results also suggest that we remain at the momentin a liminal zone; standards of care protect physicians, but so does
AI. Results are uncertain and are likely to remain so (at least until
and unless the use of AI itself becomes a new standard of care).
These are important results of a well-designed study. Of course,
there are complications in translating a study of this sort into real life
and investigating the implications. With respect to how legal cases
will turn out, there are 2 axes of additional complexity: how the law
circumscribes the role of a jury, and how the jury functions in practice.
Although the jury is central to liability, very few civil cases
actually make it to juries (3). Most cases settle—though settlement
negotiations are certainly informed by what juries are likely to do.
Perhaps more importantly, judges have multiple opportunities toresolve cases against patients without trial. If in our hypotheticalscenarios, among other things, a judge determines no reasonablejuror could find for the plaintiff because it is beyond dispute thatthe physician followed the standard of care, the judge would not
let the case go to the jury and instead would find for the physician.
To illustrate: in scenario 3 in Figure 1, a physician followed thestandard of care and rejected the AI’s nonstandard advice. Because the physician unambiguously followed the standard of care,even though prospective jurors found this to be the second-leastreasonable scenario, a real case with these facts would likely beresolved in favor of the physician before it got to the jury. To be
sure, battling medical experts often dispute what the standard of care

JUROR VIEWS ON MEDICAL AI LIABILITY • Price et al.

15

as Tobia et al. note, the question of adoption will
depend on more than liability; the impact of AIon quality and cost of care may matter more.
Moreover, even if the use of AI increased thelikelihood of liability for any given injury (aresult Tobia et al. suggest is unlikely), if AI
decreases the rate of error, as all hope it will,
liability might still cut in favor of adoption.
A more policy-oriented question asks whether
negligence is an effective framework for governing quality and redressing injuries when AI isinvolved. Selbst argues that the inscrutability of
FIGURE 1. Comparison of potential legal outcomes under current law according to AI makes negligence fundamentally problemanalysis of Price et al. (2) and empiric study findings of Tobia et al. (1). ★ 5 agreement atic because it interposes an obfuscatory layerthat physician decision was reasonable (highest is ★★★★; lowest is ★). Greater between human actors and the consequences ofagreement indicates lower likelihood of liability; ☑ 5 study results confirming our their actions (7). Among other things, AI cananalysis of current tort law; ⍰ study results suggesting that jury outcome may also replicate biases that exist in the medical system
be liability; ⊠ study results suggesting that jury might decide no liability.
(8), but in such a way that the tort system cannot
identify that bias (7,9). Questions of causation
actually requires, but a clear standard of care—whether established are also likely to be endemic, raising questions about the right frameby documents or experts—will mean no physician liability, even if a work for AI going forward. Rather than negligence, should a no-fault
jury would prefer the physician had followed the AI instead.
liability system be imposed for AI in medical or other contexts, orThe standard of care is especially protective of physicians because it is should some other system be created?
Finally, the study raises questions about AI liability more broadly:
not unitary; if physicians can show that they have followed the standard
as practiced by a ‘‘respectable minority’’ of the medical field, that too will does the protective effect of following AI recommendations also
be enough to prevent liability (4). So even when the use of AI becomes apply to other domains, such as automated vehicles? Here we suggest
the standard of care (5), older rubrics will protect physicians who follow caution. In medicine, some AI may be an inscrutable decisionmakerthem for some time. None of this is captured in the study design Tobia et granted some level of trust and deference—but so, often, is a phyal. have adopted, which asks how a juror would view the case; in the U.S. sician. For domains where the impacted individuals have their ownsystem, only a tiny fraction of such cases ever make it to a juror. That understanding of the underlying system, deference to algorithmic
said, settlement does typically take place in the shadow of law, such that recommendations might not develop as easily as deference can be
anticipation of what a juror might do will feed back into settlement.
transferred between different inscrutable actors in medicine.
A second complication arises within the role of the jury. Unlike invignette studies, jurors do not make decisions in a vacuum; they are DISCLOSURE
instructed in the law by the judge and then engage in deliberative
This work was supported by a grant from the Collaborativedecision making. From observing only individual juror decisions,
there is no reliable way to predict how collective jury verdicts will Research Program for Biomedical Innovation Law, a scientificallyarise, though median jurors do tend to predict damage outcomes (6). independent collaborative research program supported by a Novo
To some extent, these are standard critiques of mock juror Nordisk Foundation grant (NNF17SA0027784). I. Glenn Cohen
research designs, and Tobia et al. have been careful in their design serves as a bioethics consultant for Otsuka Pharmaceuticals on its
Abilify MyCite digital medicine product and on the ethics advisoryand not overclaimed.
board for Illumina. No other potential conflict of interest relevant tothis article was reported.
FUTURE WORK AND BROADER IMPLICATIONSThe study of Tobia et al. should serve as a useful beachhead forfurther work to inform the potential for adoption of AI into medical practice. We note 3 avenues: hospital adoption, negligence more
broadly, and AI deference outside medicine.
One key question, and one that Tobia et al. raise, is how liability
factors directly into hospital decisions to adopt AI systems. With
respect to liability, how do prospective jurors weigh the decisions
described here, in which a physician interacts with AI, against
situations in which there is no AI in the picture at all? If, as Tobia
et al. suggest, following AI provides a different and independent
liability-lessening effect, then hospitals may well be more eager toadopt AI systems. But that question should be independentlyevaluated. The relationship between hospital willingness to adopt
an AI and the ramifications for physicians who do not follow it iscomplicated, mediated by (among other things) malpractice insurance,
reimbursement rules, and whether the use of AI becomes part of thestandard of care that hospitals themselves must follow. Stepping back,

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THE JOURNAL OF NUCLEAR MEDICINE • Vol. 62 • No. 1 • January 2021