The researchers set out to map where, in the machinery of modern healthcare delivery, AI-related harms actually occur. Rather than treating AI as a single black box that a doctor either accepts or rejects, they categorized the cases according to the specific clinical tasks physicians perform: diagnosing conditions, ordering and interpreting tests, planning treatment, triaging patients, documenting and communicating information, and coordinating care across institutions. The distribution was revealing. Harms clustered not only around diagnostic tools — the category that dominates public imagination and much of the regulatory conversation — but also around administrative and coverage systems, including AI used by insurers to deny or curate care, and screening tools deployed by state agencies to flag families for investigation.

That breadth matters because it exposes a structural blind spot in both technology design and legal doctrine. Much of the existing scholarship on AI liability in medicine has centered on a deceptively simple question: if an AI tool outperforms a doctor, does reliance on it constitute negligence, or does deviation from it? Prior work, including widely cited analyses in JAMA and law reviews, has debated whether physicians could face liability for ignoring a competent algorithm or for deferring to a flawed one. Mansi and Riedl argue that this physician-centered frame is too narrow to describe what actually happens when patients are harmed. An insurance company’s algorithm may terminate home-care coverage; a state screening tool may incorrectly flag a parent for child-welfare investigation; a triage system may misdirect emergency care. In each scenario, the patient’s injury emerges from decisions distributed across an organization, with the physician often having little visibility into — or control over — how the AI was configured, trained, deployed or applied.

The legal cases the researchers analyzed come from a meticulous triangulation of three public databases: the Database of AI Litigation maintained at George Washington University Law School, the Health Litigation Tracker, and the AI, Algorithmic and Automation Incidents and Controversies repository, a community-maintained archive of documented harms. As of February 2025, all three resources are freely accessible online, and the researchers referenced individual cases through unique identifiers, centering documented harms rather than the specific systems involved. The case-study approach allowed the authors to reconstruct, case by case, how harm materialized, which stakeholders were positioned between the patient and the algorithm, and what barriers the patient encountered in seeking recourse.

Those barriers, the study finds, are both legal and technological, and they reinforce one another. On the legal side, liability structures remain tethered to a model of medical malpractice that presumes a human decision-maker with enough information and discretion to have acted differently. When an algorithm’s output is embedded in a payer’s coverage workflow or a state agency’s screening pipeline, a patient’s lawyer must identify which entity’s conduct was negligent, prove causation, and often overcome trade-secret protections that keep the model’s logic opaque. On the technological side, the tools themselves are rarely designed for contestability. Explanations, where they exist, are built for clinicians — and even for clinicians, a substantial body of human-computer interaction research shows that explainability features are frequently underused, misunderstood or shaped more by regulatory checkbox than by genuine decision support. They are almost never built for the people who most need them when things go wrong: patients and their advocates.

Here the study makes its most provocative move. Mansi and Riedl argue that lawyers working for patients should be recognized as legitimate users of AI systems — and, in a sense, as intermediaries whose needs deserve to shape the design of medical AI from the ground up. If an explainability interface cannot help a patient’s legal team understand which stakeholder made which consequential decision, what data the model relied on, and where in the pipeline the error propagated, then the tool has effectively insulated everyone but the patient from scrutiny. The authors connect this to a growing research program on legally-informed explainable AI, which aims to produce explanations aligned with the evidentiary demands of legal proceedings rather than with the aesthetic of transparency that currently dominates industry practice.

The researchers also draw a pointed lesson from documented incidents involving coverage algorithms. Reporting on how Medicare Advantage plans have used AI tools to cut off care for seniors, alongside legal scholarship on regulating healthcare coverage algorithms, illustrates the pattern the study identifies: harms arise at the intersection of commercial incentives, algorithmic automation and fragmented oversight. A patient denied post-acute care by an algorithmic coverage tool may appeal through administrative channels, sue, or simply suffer silently. Each path demands different evidence, different expertise and different institutional access — resources that are unevenly distributed among the very populations most likely to be harmed.

What can be done? The authors propose reform on two fronts. First, liability structures should evolve to reflect the distributed reality of AI-mediated care. They point to legal scholarship exploring theories such as the common enterprise theory of liability, which could allow responsibility to be shared across the ecosystem of actors — developers, payers, facilities, regulators — that jointly shape how a tool affects patients. Distributed governance frameworks for medical AI, and regulatory thinking that treats software as a medical device within a whole system rather than as a standalone artifact, offer templates for how accountability could be allocated more realistically. Second, AI tools for healthcare should be designed for contestability by default: systems should log which stakeholders shaped their outputs, generate explanations usable in legal contexts, and support advocates — including patients’ lawyers — in reconstructing what happened when harm occurs. Contestable AI, an emerging design philosophy in human-computer interaction, offers concrete principles: make decisions reversible, make the grounds of decisions accessible, and make the pathway to human review visible to affected people.

The stakes of getting this wrong extend beyond individual lawsuits. If patients cannot seek recourse, there is no corrective feedback signal pushing developers and deployers to fix flawed systems; courts become the only place where the accumulated evidence of algorithmic failure can surface, and only for those few patients wealthy or persistent enough to get there. Conversely, if accountability is designed into the system — technologically through auditable, contestable tools, and legally through liability frameworks that track the actual distribution of decision-making power — then recourse becomes possible without requiring every patient to become a litigant. The study’s authors, whose work was supported in part by the National Science Foundation, frame this as a shift from physician-centered to patient-centered accountability, a reorientation that demands new collaboration between lawyers, technologists, clinicians and regulators.

For a healthcare system rapidly absorbing AI into triage, diagnosis, documentation and coverage decisions, the message of the litigation record is uncomfortable but clear: the technology is arriving faster than the accountability structures needed to govern it, and the people bearing the consequences are the ones the current framework serves least. Mansi and Riedl’s analysis of 31 cases turns courtroom silence into a design specification — a map of where AI harms happen in healthcare delivery, and a blueprint for tools and laws that could finally let patients fight back.

Subject of Research: Analysis of US litigation involving medical AI tools and patient-centered accountability for AI-related harms in healthcare delivery.

Article Title: Implications of current litigation on the design of AI tools for healthcare delivery and related legal frameworks

Article References: Mansi, G., & Riedl, M. (2026). Implications of current litigation on the design of AI tools for healthcare delivery and related legal frameworks. Nature Health. https://doi.org/10.1038/s44360-026-00186-y

Image Credits: AI Generated

DOI: 10.1038/s44360-026-00186-y

Keywords: medical AI, healthcare litigation, AI liability, patient recourse, explainable AI, contestable AI, healthcare delivery, algorithmic harms, coverage algorithms, legal frameworks, patient-centered accountability, Nature Health

The study examined Burundi, Ethiopia, Malawi, Mozambique and Namibia, five countries that differ enormously in wealth, food security and education system maturity, yet all of which operate national or large-scale school feeding programmes. The researchers combined nationally representative household surveys with administrative education data, comparing children who received school meals with children who did not, both at the national level and across subnational regions, and separately for boys and girls. Their central finding is striking: beneficiaries of school feeding would gain, on average, 1.3 additional years of schooling compared with non-beneficiaries, with country-level estimates ranging from a low of 0.6 years to a high of 2.3 years.

The logic behind these gains is grounded in decades of nutrition and education research. Chronic food insecurity pushes families toward painful trade-offs, and one of the most common responses is to withdraw children from school, either because the family cannot afford the indirect costs of attendance or because a hungry child cannot concentrate, walk long distances to class or sustain the physical demands of the school day. School meals act as a form of social protection, transferring resources directly to households while strengthening the incentive to keep children enrolled. When a nutritious meal arrives reliably at school, the marginal cost of sending a child falls, and the marginal benefit rises, making dropout less likely and grade progression more likely.

Translating additional schooling into money is where the study becomes an economic argument as much as an educational one. The researchers estimated the returns to education using adapted Mincer equations, the workhorse framework of labour economics that relates log earnings to years of schooling and experience, supplemented where appropriate by published estimates from the global literature. Across the five countries, each additional year of education was associated, on average, with 13.3 per cent higher earnings, with estimates ranging from 9.5 per cent to 19.5 per cent. Combining the schooling gains with these returns, the team projected that school feeding beneficiaries would see lifetime earnings approximately 25.6 per cent higher than comparable children who did not receive meals, an enormous cumulative effect originating from what is often regarded as a modest, routine intervention.

The disaggregated analysis reveals where those benefits concentrate. Because the researchers modelled impacts both nationally and subnationally, they could identify the regions within each country where school feeding is likely to deliver the largest educational payoffs. Unsurprisingly, the greatest gains appear in disadvantaged areas, where baseline dropout rates are high, food insecurity is severe and alternative social protection is thin. In such settings, the meal programme effectively compensates for broader gaps in the social safety net. The analysis by sex also allows the programme’s role in closing gender gaps in schooling to be examined, an important consideration given that girls in low-income settings are often the first to be withdrawn from school when household resources tighten, and given the well-documented downstream benefits of girls’ education for fertility, child mortality and intergenerational health.

The methodological machinery behind these estimates is worth unpacking, because the authors are candid about its limits. In Ethiopia and Malawi, where rich household survey data were available, including the Ethiopian Socioeconomic Survey and multiple rounds of the Malawi Integrated Household Survey, the team estimated transitions between schooling states directly from observed data. For Burundi, Mozambique and Namibia, they constructed a documented transition-matrix framework, using education management information system data and national statistics to model how beneficiary and non-beneficiary cohorts progress through the school system. The schooling gains were then propagated through Mincerian or literature-based returns to produce projected earnings differentials. The analytic code, along with processing and estimation pipelines for the countries with public data, has been released openly on GitHub, an unusually transparent step that allows other researchers to reproduce, stress-test and extend the modelling.

That transparency matters because the authors themselves label their headline numbers as observational plausible upper bounds rather than causal guarantees. Beneficiaries and non-beneficiaries are not randomly assigned; school feeding programmes are typically targeted, deliberately or otherwise, toward needier regions, which complicates naive comparisons. The team ran extensive sensitivity analyses, visualised in their published heat maps, to explore how alternative assumptions about returns to education, transition probabilities and programme coverage affect the results, and the qualitative conclusion, that school feeding meaningfully increases retention and lifetime earnings, is robust across specifications. Even so, they explicitly call for new longitudinal quasi-experimental studies, ideally exploiting staggered programme rollouts or sharp eligibility thresholds, to pin down causal estimates with the precision that policy commitments of this scale deserve.

Even interpreted as upper bounds, the findings arrive at a consequential moment. School feeding has expanded dramatically worldwide, reaching hundreds of millions of children, and is increasingly championed not merely as a nutrition intervention but as a cross-sectoral investment spanning health, education and social protection. Previous systematic reviews and meta-analyses have found positive but heterogeneous effects of school meals on enrolment, attendance and learning, and value-for-money studies have been completed or are underway in each of the five study countries under the Research Consortium for School Health and Nutrition, funded through an original grant from the Norwegian Agency for Development Cooperation with follow-on support from the World Food Programme and other partners. The new Nature Health analysis adds a lifetime-earnings dimension to that evidence base, converting years of schooling into the currency that finance ministries ultimately weigh.

The economic framing also reframes the cost debate. School feeding programmes require sustained procurement of food, logistics and administration, and critics have long questioned whether the resources might deliver more if spent on textbooks, teacher training or direct cash transfers. But the earnings projections here suggest that the benefits of keeping children in school compound over entire working lives, and that they accrue disproportionately to the poorest regions, where the marginal social value of a retained pupil is highest. There are broader dividends as well, beyond the individual wage premium: parental education is strongly linked to child survival, and education shifts fertility patterns and population health in ways that markets do not fully capture. A meal that keeps a girl in school for two extra years may influence the health and schooling of the next generation.

For the five countries studied, and for the many others watching, the message is that school meals should be evaluated with the same seriousness as any major public investment, using data on who benefits, where the gains are largest and what the long-run returns look like. The Harvard-led team’s open data and code make that kind of evaluation more feasible than ever. What remains is the harder scientific task they themselves identify: following real cohorts of children over time, with designs that isolate the causal effect of the meal from the poverty that motivates it. Until then, the numbers stand as a provocative estimate that one of the oldest interventions in the development toolkit, feeding hungry children at school, may quietly be one of the highest-return investments a government can make.

Subject of Research: Estimated effects of school feeding programmes on educational attainment and lifetime economic returns in Burundi, Ethiopia, Malawi, Mozambique and Namibia

Article Title: Impact of school feeding programmes on educational attainment and economic returns in five African countries

Article References: Forzy, T., Ramponi, F., Iversen, I., Durizzo, K., Kim, S., Gautam, P., Avallone, S., Memirie, S. T., Habtemichael, M., Getnet, F., Wogasso, Y., Peterson, H., Masamba, K., Saka, A., Nkengurutse, J., Ndayitwayeko, W.-M., Ntunzwenimana, M., Bigirimana, L., Mbengue, M., … Verguet, S. (2026). Impact of school feeding programmes on educational attainment and economic returns in five African countries. Nature Health. https://doi.org/10.1038/s44360-026-00176-0

Image Credits: AI Generated

DOI: 10.1038/s44360-026-00176-0

Keywords: school feeding, educational attainment, lifetime earnings, sub-Saharan Africa, returns to education, dropout, social protection, Mincer equation, food security, Nature Health, Harvard Chan School, education economics

The methodological innovation at the heart of the study lies in what the authors call health impact event attribution. Traditional extreme event attribution, which has matured rapidly over the past decade, typically asks how climate change altered the probability of a meteorological anomaly such as a heatwave, using metrics like regional temperature maxima. But a hotter day does not translate directly into a death, and the relationship between temperature and mortality varies enormously with geography, climate, age structure and adaptation. The new approach closes that gap by coupling attribution statistics to epidemiological exposure-response functions calibrated for each of 34 European countries. Rather than asking how much more likely the 2022 heatwave was, the team asked how much more likely the mortality event itself was, the actual human toll, in a climate warmed by roughly 1.2 degrees Celsius compared with a pre-industrial baseline.

Technically, the researchers constructed generalized extreme value distributions for both temperature and heat-related mortality, allowing them to estimate return periods, how often an event of a given magnitude would be expected to occur, under current and counterfactual pre-industrial conditions. The ratio of these probabilities, known as the probability ratio, then expresses how much climate change has multiplied the odds of an extreme. Crucially, the mortality side of the analysis was built on weekly death counts from Eurostat, publicly available data that are updated in near real time. Temperature inputs came from the ERA5-Land reanalysis produced by the European Centre for Medium-Range Weather Forecasts, while global mean surface temperature changes were drawn from NASA’s GISTEMP record. The reliance on weekly rather than daily mortality data was validated using recent methodological work showing that temporally aggregated health data can yield unbiased temperature-mortality estimates, a finding that dramatically expands where such analyses can be performed.

The results reveal a continent divided. Across Europe as a whole, 2022-like heat-mortality events became substantially more probable, but the amplification in Southern Europe dwarfed the continental average. The Mediterranean region, already identified in the scientific literature as a climate change hotspot, experienced nonlinear increases in the likelihood of deadly heat, meaning that each additional fraction of a degree of warming multiplies the risk faster than the last. The study also projected forward, estimating probability ratios at 1.5 and 2.0 degrees Celsius of global warming, the benchmark thresholds of the Paris Agreement. Those projections show the risk continuing to climb steeply, with Southern Europe again bearing the steepest increases. The nonlinear character of these changes is particularly troubling because it implies that modest additional warming will produce disproportionately large jumps in mortality risk, compressing the time available for societies to adapt.

Equally significant are the demographic disparities the analysis uncovered. When the researchers stratified their models by age, they found that the elderly, particularly those aged 80 and older, faced sharply elevated probability ratios compared with younger populations. This pattern reflects well-documented physiological vulnerabilities, including diminished thermoregulatory capacity, higher prevalence of chronic disease, and greater use of medications that impair the body’s ability to cope with heat stress. Prior research has also documented sex-specific differences in heat vulnerability and adaptation, and the new framework’s ability to dissect such heterogeneity is one of its principal strengths. The authors emphasize that these findings make the case for population-specific analyses: a national average can conceal the fact that a small, highly vulnerable subgroup absorbs a wildly disproportionate share of the climate-driven mortality increase.

The 2022 summer serves as the study’s anchor case because it was, by most measures, the hottest summer ever recorded in Europe. Spain registered its warmest summer in its historical series, the United Kingdom breached 40 degrees Celsius for the first time, and the Copernicus Climate Change Service documented the season as a continental anomaly. Previous work by several of the same authors estimated that the summer of 2022 caused more than 60,000 heat-related deaths across Europe, while companion studies attributed a substantial fraction of that toll directly to anthropogenic warming. The new attribution analysis reframes those mortality counts in probabilistic terms: without human influence on the climate, an event of that lethality would have been extraordinarily rare, whereas in today’s climate it has become a recurring hazard with a return period measured in years rather than centuries.

What distinguishes this work from earlier impact attribution efforts is not only its scope, spanning 34 countries, but its speed and reproducibility. Because the mortality data are public and updated weekly, and because the analytical code has been released openly on GitHub, the framework can in principle be deployed rapidly after any extreme heat episode, transforming attribution from a retrospective academic exercise into a near-real-time public health tool. The authors argue that this capability could feed directly into health emergency forecasting systems, several of which are already being developed and tested in Europe, allowing authorities to anticipate and respond to deadly heat before the peak toll is counted. Rapid attribution of health impacts also carries weight beyond epidemiology, providing quantified evidence relevant to climate litigation, loss-and-damage negotiations and the legal and moral accounting of emissions.

The study arrives amid a broader scientific consensus that heat has become Europe’s leading weather-related killer. The Lancet Countdown and the World Meteorological Organization have both documented rising heat mortality, and modeling studies covering hundreds of European cities project that the burden will grow substantially without aggressive adaptation. Yet adaptation itself remains uneven. Research from Spain and the Netherlands shows that populations can shift their minimum mortality temperature over time, a signature of acclimatization, but the pace of such physiological and behavioral adaptation lags far behind the pace of warming. Heat-health warning systems exist across much of Europe but vary widely in coverage, trigger thresholds and effectiveness. The new findings sharpen the argument that adaptation investments, from cooling centers and urban greening to occupational heat protections and targeted outreach to the elderly, should be prioritized precisely where and for whom the probability ratios are highest.

Ultimately, the study delivers a double message. First, the fingerprint of climate change is now legible not just in thermometers but in mortality statistics: the deaths of thousands of Europeans in recent summers are statistically attributable to emissions, with Southern Europeans facing risks an order of magnitude beyond the continental average. Second, the tools needed to see that fingerprint are now fast, transparent and grounded in freely available data, meaning that the era of waiting years to understand the human cost of an extreme summer is over. As global temperatures continue to rise toward and potentially past the 1.5-degree threshold, the nonlinear escalation of heat-mortality risk documented in this analysis offers both a warning and a roadmap. The regions and populations identified as most vulnerable are known, the data streams needed to monitor the threat in real time exist, and the remaining variable is the speed with which societies choose to act on the evidence.

Subject of Research: Attribution of extreme heat-related mortality events in Europe to anthropogenic climate change

Article Title: Extreme event attribution for heat-related mortality due to anthropogenic climate change across Europe

Article References: Beck, T. M., Gudmundsson, L., Schumacher, D. L., Seneviratne, S. I., Achebak, H., & Ballester, J. (2026). Extreme event attribution for heat-related mortality due to anthropogenic climate change across Europe. Nature Health. https://doi.org/10.1038/s44360-026-00193-z

Image Credits: AI Generated

DOI: 10.1038/s44360-026-00193-z

Keywords: extreme event attribution, heat-related mortality, climate change, Europe, heatwaves, Nature Health, epidemiological models, Southern Europe, public health, adaptation, probability ratio, 2022 heatwave