The study, led by Joseph Conteh of the University of New Mexico together with Naim Kapucu, Eunice F. Ebevuhe, and Oluwapelumi Wumi Adeniji, was conducted and reported in accordance with the PRISMA 2020 reporting guideline, with study selection governed by predefined PICOS eligibility criteria. The team searched four databases—PubMed, Web of Science, Scopus, and Google Scholar—for peer-reviewed articles published from January 2017 through January 2026, with final searches completed between January 15 and 18, 2026. Of 1,065 records identified, just 22 studies met the eligibility criteria. Methodological quality was appraised with design-appropriate tools: the Critical Appraisal Skills Programme checklist for qualitative and certain observational designs, the Newcastle-Ottawa Scale for cohort and case-control studies, and QUADAS-2 for diagnostic accuracy studies. A narrative synthesis was followed by a structured regulatory gap analysis of federal statutes, regulations, and agency guidance, in which a gap was recorded only when at least two independent sources converged. Inter-rater agreement at the title and abstract screening stage was substantial, with Cohen’s kappa of 0.84.

The review’s central diagnosis is that the federal regulatory architecture was built for a fundamentally different technological paradigm. Authority over hospital AI is dispersed across agencies that each hold a partial slice of oversight: the FDA evaluates safety and effectiveness of Software as a Medical Device through pre-market review; the Office of the National Coordinator for Health Information Technology certifies health IT and mandates transparency for predictive decision-support interventions through the HTI-1 rule; the Centers for Medicare and Medicaid Services shapes adoption through conditions of participation, quality reporting, and payment policy; HIPAA governs the privacy and security of health data; and the Federal Trade Commission polices deceptive or unfair AI claims. In a decentralized federal environment, state legislation has emerged as a supplementary force, producing a patchwork landscape—California, for example, has established some of the nation’s strictest privacy and transparency standards, requiring healthcare institutions using AI-assisted diagnostic tools to inform patients about the system’s functionality, diagnostic basis, and potential risks.

The most technically striking finding concerns the chasm between pre-market validation and real-world performance. A COVID-19 chest radiograph AI diagnostic tool that achieved an area under the receiver operating characteristic curve greater than 0.95 on open datasets dropped to approximately 0.70 after prospective deployment across multiple hospital settings, varying from 0.66 to 0.74 across time quartiles over 19 post-deployment weeks. Predictive models delivering AUC values of 0.96 for readmission prediction and 0.83 from roughly 280,000 cases were tested only on retrospective data without external validation. The review is careful to distinguish deficiencies in the evidence submitted to the FDA from deficiencies in FDA requirements themselves—many validation gaps originate in the pre-submission research pipeline rather than in statutory standards, a distinction that matters because the remedies differ. Even stringent validation systems such as APPRAISE-AI, which rates methodology, results, and reproducibility, do not fully account for temporal and situational variation in clinical settings.

When regulatory-grade evidence does exist, the results can be dramatic. The KATE triage system outperformed nurses by 26.9% in general acuity assignment across 729 reviewed patient records, decreased undertriage from 19.8% to 9.7% and overtriage from 20.4% to 14.4%, and captured 1,880,841 features at 12.79 per encounter with 98.47% accuracy. An FDA-authorized AI tool for pulmonary embolism detection achieved 99.9% negative predictive value across 11,736 real-world imaging tests and slashed worklist detection times from 7,714 minutes to 87 minutes while reducing missed cases from 44.8% to 2.6%. A machine learning sepsis prediction algorithm enabled antibiotic delivery within 2.76 hours, cutting in-hospital mortality from 21.3% to 9.0%—a 58% relative reduction. But these successes remain exceptions rather than the norm, and the review found that AI’s relationship with CMS quality metrics is mixed: in a longitudinal observational study spanning 70 CMS quality indicators from 2022 to 2025, hospitals using AI showed positive trends for pneumonia mortality and hospital-acquired conditions but negative trends for readmission rates and sepsis treatment compliance. Because these are observational associations, they do not establish causal effects.

The structured gap analysis identified four critical, structurally interconnected failures. The first is the post-market monitoring gap: only 58.1% of hospitals in a national sample performed post-implementation monitoring in 2024, and the majority of those lacked continuous drift detection. In related survey data, 47.4% of hospitals did not evaluate post-deployment accuracy, 51.4% had no bias assessment protocols, and in 61% of hospitals post-deployment accuracy was assessed but bias was not, with all monitoring remaining sporadic. The second is the equity assurance gap. The training dataset of a readmission prediction model included less than 2% Hispanic and Asian participants while Caucasian patients made up more than 75%, with no subgroups evaluated for differential performance. The KATE system showed an 11.4 percentage point accuracy disparity between adults (78.5%) and children (67.1%) without subgroup analyses for race, ethnicity, or socioeconomic status. The review also distinguishes algorithmic bias from structural adoption disparities: larger hospitals had a 15.2% higher probability of adopting machine learning than smaller ones, health system-owned hospitals had a 26.8% higher probability than independently owned ones, and regions of high healthcare need in low-adoption areas had persistently lower adoption probabilities—including ratios of 0.75 for primary care Health Professional Shortage Areas and 0.74 for high Area Deprivation Index areas.

The third failure is the accountability clarification gap, which the review dissects into four frequently conflated dimensions: legal liability, clinical responsibility, institutional governance, and vendor obligations. Radiologists bore responsibility for diagnosing pulmonary embolism, yet responsibility allocation for 12 cases of missed diagnosis remained unclear. An AI-clinician hybrid framework for COVID-19 diagnosis did not determine responsibility distribution among developers, informaticians, and treating physicians, and a randomized clinical trial of sepsis prediction specified no clear distribution of responsibility in case of patient harm. The problem is compounded by market concentration: 79% of hospitals use predictive models created by electronic health record vendors, yet no frameworks exist for determining accountability between hospitals, developers, vendors, and clinicians. The fourth is the governance capacity gap, which operates as a cross-cutting constraint across the others. Only 65% of hospitals used predictive models and only 44% assessed biases, with higher implementation rates concentrated among larger and more profitable institutions. Even best-practice tools illustrate the barrier: DEPLOYR required Azure Functions, specialized Python libraries, data warehouse access with institutional review board approval, and registration at the Epic App Orchard, while the MedPerf federated benchmarking initiative demanded collaboration between more than 20 firms, 20 universities, and 9 hospitals across 13 countries, encountering regulatory, legal, and reputational barriers throughout.

The documented harms lend urgency to these technical findings. The widely cited study by Obermeyer and colleagues demonstrated that a commonly used healthcare algorithm systematically underestimated illness severity among Black patients, resulting in only 17.7% being identified as eligible for advanced care programs when a corrected, unbiased algorithm would have identified 46.5%. Insufficiently externally validated sepsis prediction technologies have generated alert fatigue that paradoxically diminishes clinician responsiveness to genuine patient deterioration signals. Survey data show that 73.1% of clinicians regarded lack of trust, transparency, or bias as critical barriers to implementation, and evaluations of large language models found that GPT-4 provided consistent responses in only 67 of 80 clinical scenarios (83.8%), with both GPT-3.5 and GPT-4 demonstrating weakness in recognizing diversity, equity, and inclusion principles. The review notes that the HTI-1 rule’s transparency mandates, while a positive step, suffer from underdeveloped enforcement mechanisms and unstandardized disclosure content.

International comparisons sharpen the picture. The European Union Artificial Intelligence Act imposes binding pre-market conformity assessment for high-risk AI systems, including most medical AI, and centralizes enforcement in designated market-surveillance authorities while assigning explicit obligations to both providers and deployers. U.S. requirements, by contrast, remain dispersed across FDA, ONC, and CMS authorities, with enforcement divided among federal and state actors holding partial jurisdiction, and post-market mechanisms such as Predetermined Change Control Plans still in early implementation. Drawing on these models, the authors propose a lifecycle governance framework with six evidence-linked components: rigorous pre-market clinical validation with standardized benchmarking, mandatory post-deployment monitoring with automated drift detection and performance alerts, standardized transparency and explainability aligned with the HTI-1 rule, equity-centered assessment protocols requiring subgroup analysis and bias reporting, clear accountability frameworks delineating responsibility among developers, vendors, institutions, and clinicians, and scalable governance infrastructure with targeted support for under-resourced settings.

The authors acknowledge key limitations: the retrospective registration of the review protocol on the Open Science Framework, the restriction to English-language publications, database coverage limited to four sources while Embase, CINAHL, and ClinicalTrials.gov were excluded, and the heterogeneity of the included evidence, much of it retrospective and single-center. International studies from Canada, Saudi Arabia, and the Netherlands were used to contextualize rather than as direct evidence of U.S. regulatory performance. Still, the core conclusion stands on firm ground: industry projections indicate AI-enabled technologies will influence decision-making in most major U.S. health systems within the coming decade, and if the identified gaps persist as deployment scales, the risks to patient safety, equity, and clinical trust will grow commensurately. The review closes by grounding its call in medicine’s oldest ethical principle—primum non nocere, first, do no harm—arguing that only a national, lifecycle-focused, systems-based governance approach can ensure that hospital AI remains safe, ethical, and trustworthy as it reshapes clinical care.

Subject of Research: A systematic review and regulatory gap analysis of AI governance in U.S. hospitals, identifying four critical regulatory gaps and proposing a lifecycle governance framework.

Article Title: A framework for safe, ethical, and trustworthy AI deployment and integration in hospitals: a systematic review and regulatory gap analysis with emphasis on the U.S. regulatory context

Article References: A framework for safe, ethical, and trustworthy AI deployment and integration in hospitals: a systematic review and regulatory gap analysis with emphasis on the U.S. regulatory context. (n.d.). https://doi.org/10.3389/fdgth.2026.1955958

Image Credits: AI Generated

DOI: 10.3389/fdgth.2026.1955958

Keywords: AI governance, hospital AI, FDA regulation, health equity, patient safety, systematic review, regulatory gaps, digital health, framework, safe, ethical, trustworthy

The agricultural case for sludge is genuinely strong when the material is clean. Field studies cited in the review show that sludge application can raise crop yields by as much as 60 percent, rivaling the effect of fertilizers made from non-renewable resources. Two years of sludge amendment increased not only soil organic matter but also the stability of soil aggregates, improving soil structure in ways that conventional mineral fertilizers cannot. Biosolids even boosted the phenolic content and antioxidant activity of sweet basil, hinting at quality benefits beyond sheer yield. In a world facing dwindling phosphorus reserves and a nitrogen fertilizer crisis, the logic of recycling nutrients from human waste back into farmland is compelling. Circular-economy advocates argue that every tonne of sludge diverted to agriculture is a tonne of mined phosphate rock that does not need to be extracted.

The problem is what else rides along with those nutrients. Heavy metals, including cadmium, zinc, copper, chromium, nickel, arsenic, silver, and lead, make up roughly 0.5 to 2.0 percent of sludge on a dry-weight basis, and they originate from both industrial discharges and everyday household plumbing. Once these metals enter soil they are not biodegradable and can persist in the environment for decades, migrating through soil profiles into groundwater and accumulating in the edible tissues of crops. One influential study found that while sludge boosted yields of mung bean by up to 60 percent, it simultaneously raised the accumulation of lead, nickel, and cadmium in the grain, undermining its suitability as food. Under European rules for organic-mineral fertilizers, permissible limits are 100 milligrams of chromium, 5 milligrams of cadmium, 60 milligrams of nickel, 140 milligrams of lead, and 2 milligrams of mercury per kilogram, but the actual metal content of a given sludge depends heavily on its origin, and the same production technology can deliver strikingly different results depending on whether the process runs at laboratory, pilot, or full industrial scale.

Pathogens constitute a second, more insidious barrier. Municipal sludge is a biological concentrate of fecal bacteria such as Escherichia coli and Salmonella, enteric viruses including rotavirus, norovirus, and hepatitis A, protozoan parasites like Giardia intestinalis, and fungal genera such as Aspergillus and Penicillium. Farmers handling sludge-based fertilizers report skin allergies, digestive ailments, and respiratory infections, and communities living near fields where sludge is sprayed face elevated risks of pharyngitis, coughing, and shortness of breath from airborne transmission. Contaminated soil can also shed pathogens into surface water and groundwater during rainfall, threatening drinking supplies and aquatic biodiversity. The review stresses a subtle analytical problem: pathogen counts measured with culture-based methods versus molecular assays such as quantitative PCR are not directly comparable, and detecting viral genetic material does not necessarily mean the virus is still infectious. Comparisons across studies are further muddied by differences in sludge stabilization history, storage conditions, and seasonal variation in the incoming wastewater.

Organic micropollutants form perhaps the most chemically diverse contaminant group. The most frequently detected compounds include linear alkylbenzenesulfonates from detergents, the plasticizer di-ethylhexylphthalate, nonylphenols, polychlorinated biphenyls, dioxins and furans, and polycyclic aromatic hydrocarbons, which have been measured at concentrations ranging from 0.7 to 75.2 milligrams per kilogram in sludge. Persistent compounds such as DEHP and PAHs are of particular concern because they have been linked to cancer, reproductive damage, and metabolic disease. Pharmaceuticals add another layer: global antibiotic consumption rose 65 percent between 2000 and 2015, and most antibiotics pass through the human body unmetabolized, ending up in sludge at concentrations from micrograms to milligrams per kilogram. When antibiotic-laden sludge is applied to soil, it exerts selective pressure that fosters antibiotic-resistant bacteria, suppresses microbial diversity, and can inhibit carbon mineralization, nitrogen cycling, and enzyme activity. Natural and synthetic steroid hormones, active at vanishingly low concentrations, raise endocrine-disruption worries, while persistent pesticides accumulate in sludge solids because of their hydrophobic chemistry.

Microplastics have emerged as the contaminant that most sharply exposes the limits of current treatment and regulation. An estimated 3.2 million tonnes of primary microplastics enter the environment annually, and between 60 and 99.9 percent of the microplastics entering wastewater treatment plants are retained in the resulting biosolids. Studies report anywhere from hundreds of thousands to several millions of microplastic particles per kilogram of sludge, depending on the analytical method and particle-size range considered. Once sludge is spread on fields, these particles, which can adsorb dyes, heavy metals, and pharmaceuticals onto their surfaces, effectively seed agricultural soils with a persistent plastic burden and a mobile carrier for co-transported pollutants. The United Nations listed microplastic pollution among the top ten environmental issues requiring immediate attention in 2017, yet quality standards for biosolids and soils, many written three decades ago, still test only for heavy metals and a handful of persistent organic pollutants. Microplastics, pharmaceuticals, hormones, and antibiotic resistance determinants fall almost entirely outside the regulatory net.

The review catalogues a battery of treatment technologies that can shrink these risks, while candidly admitting that none is a universal solution. For heavy metals, options include alkaline modification with geopolymers that immobilize metals in monolithic matrices, chemical extraction using chelating agents such as EDTA and NTA followed by sulfide precipitation, microbiological bioleaching with organisms like Acidithiobacillus ferrooxidans that achieved up to 39 percent lead removal in one study, and electrokinetic treatment that drives metal ions toward electrodes under a direct-current field. Each approach carries trade-offs: stabilization does not remove total metal mass and can reverse if soil pH or redox conditions shift, extraction generates secondary metal-rich effluents, bioleaching is slow and finicky, and electroreclamation is energy-hungry and electrode-sensitive. For pathogens, composting under controlled conditions eliminated 99 percent of Salmonella and 96 percent of fecal coliforms within a month, while thermophilic anaerobic digestion at 50 to 60 degrees Celsius denatures bacterial enzymes and outperforms mesophilic digestion. Quicklime treatment pushes pH above 12 and heats sludge to around 70 degrees, destroying organisms including Clostridium perfringens, Listeria, and Enterococcus, and hybrid schemes combining 160-degree thermal hydrolysis with anaerobic digestion came close to complete pathogen destruction, sparing only Clostridium perfringens. Irradiation with cobalt-60 or cesium-137 can shred pathogen DNA but remains rare at full scale because of cost and safety barriers.

For organic micropollutants, well-managed composting reduced hormones below detection limits in field-scale post-treatment, whereas uncontrolled open-air storage produced variable and often disappointing removal. Anaerobic digestion shows compound-specific performance, achieving over 80 percent removal for selected anti-inflammatory drugs under laboratory conditions but low or even negative removal for other pharmaceutical classes at full scale. Thermal routes are the heavy artillery: combustion at 850 to 1000 degrees destroys most organic micropollutants but transfers a fraction of them to flue-gas residues, and pyrolysis at 600 degrees reduced polychlorinated biphenyls, PAHs, and pharmaceutical and personal-care compounds by at least 99.9 percent in one comparative study, although contaminants can partition into condensates and oils that then require controlled disposal. Against microplastics, incineration removes roughly 99.9 percent of particles and pyrolysis achieves 91 to 97 percent removal, but both funnel nutrients out of closed-loop recycling into construction materials or energy, while emerging alternatives such as hydrothermal liquefaction and hyperthermophilic composting show promise yet rarely exceed 50 percent removal efficiency and remain poorly characterized in terms of degradation by-products.

The review’s most sobering findings concern the law rather than the lab. Council Directive 86/278/EEC protects soils by setting heavy-metal thresholds for agricultural sludge, but the EU fertilizer framework, Regulation 2019/1009, excludes sludge from its list of approved fertilizer ingredients precisely because of toxicity concerns, leaving farmers who wish to use it dependent on temporary national permits. Because member states write their own rules on application rates, permissible microorganism levels, microplastics, antibiotics, hormones, and pH, soil protection is inconsistent across the bloc, and internationally traded food faces uneven safety standards. The authors call for a harmonized directive specifying contaminant limits across all pollutant classes, mandatory annual soil testing for heavy metals, microplastics, hormones, and antibiotics, adoption of best available techniques, and on-site sludge processing to cut transport emissions. Public skepticism, fueled by odors and documented health complaints near land-application sites, adds a social dimension that educational campaigns and community engagement must address. The bottom line is pragmatic: sludge-derived fertilizers can genuinely anchor circular nutrient management in a phosphorus-scarce world, but only under science-based regulation, standardized analytical methods, and treatment strategies that account for contaminant fate, secondary waste streams, and real-world field conditions rather than laboratory promises alone.

Subject of Research: Contaminant risks and mitigation strategies in sewage sludge-derived fertilizers

Article Title: Assessing and Mitigating Risks of Emerging Contaminants in Fertilizer Production from Sewage Sludge: Challenges and Opportunities

Article References: Skrzypczak, D., Mikula, K., Izydorczyk, G., Samarina, T., Gil, F., Wijatkowska, A., Szyszka, D., & Chojnacka, K. (2026). Assessing and Mitigating Risks of Emerging Contaminants in Fertilizer Production from Sewage Sludge: Challenges and Opportunities. Waste and Biomass Valorization. https://doi.org/10.1007/s12649-026-03780-z

Image Credits: AI Generated

DOI: 10.1007/s12649-026-03780-z

Keywords: sewage sludge, heavy metals, pathogens, microplastics, pharmaceuticals, composting, anaerobic digestion, pyrolysis, fertilizer production, circular economy, wastewater treatment, regulatory gaps