Citations

Poor diet is the leading preventable risk factor for death worldwide, associated with over 10 million premature deaths and USD 8 trillion related costs every year. Artificial intelligence or AI is rapidly emerging as the most historically disruptive, innovatively dynamic, rapidly scaled, cost-efficient, and economically productive technology (which is increasingly providing transformative countermeasures to these negative health trends, especially in low- and middle-income countries (LMICs) and underserved communities which bear the greatest burden from them). Yet widespread confusion persists among healthcare systems and policymakers on how to best identify, integrate, and evolve the safe, trusted, effective, affordable, and equitable AI solutions that are right for their communities, especially in public health nutrition. We therefore provide here the first known global, comprehensive, and actionable narrative review of the state of the art of AI-accelerated nutrition assessment and healthy eating for healthcare systems, generated by the first automated end-to-end empirical index for responsible health AI readiness and maturity: the Responsible Health AI readiness and Maturity Index (RHAMI). The index is built and the analysis and review conducted by a multi-national team spanning the Global North and South, consisting of front-line clinicians, ethicists, engineers, executives, administrators, public health practitioners, and policymakers. RHAMI analysis identified the top-performing healthcare systems and their nutrition AI, along with leading use cases including multimodal edge AI nutrition assessments as ambient intelligence, the strategic scaling of practical embedded precision nutrition platforms, and sovereign swarm agentic AI social networks for sustainable healthy diets. This index-based review is meant to facilitate standardized, continuous, automated, and real-time multi-disciplinary and multi-dimensional strategic planning, implementation, and optimization of AI capabilities and functionalities worldwide, aligned with healthcare systems’ strategic objectives, practical constraints, and local cultural values. The ultimate strategic objectives of the RHAMI’s application for AI-accelerated public health nutrition are to improve population health, financial efficiency, and societal equity through the global cooperation of the public and private sectors stretching across the Global North and South.

Background/Objectives: Modeling analyses for colorectal cancer (CRC) screening focusing solely on the costs of screening do not fully capture the value of screening programs. We evaluated the clinical and economic effects of CRC stool-based screening tests, including impacts on cancer-related outcomes. Methods: A cohort-based decision-analytic cost-estimator model estimated outcomes for a single round of screening with next-generation multi-target stool DNA (ng mt-sDNA) test or fecal immunochemical test (FIT) from a US payer perspective. Undiagnosed cancers were assumed to become symptomatic (and detected) within 10 years. Clinical assumptions, advanced precancerous lesion and CRC prevalence, and test performance inputs were from clinical trial data. Adherence rates for initial screening and follow-up colonoscopy after a positive result were from real-world data. Input costs included the screening tests, follow-up colonoscopy (with and without polypectomy), and CRC treatment. Results: Compared with FIT, more individuals completed ng mt-sDNA (321,000 vs. 713,000, respectively), leading to the detection of more CRC cases (436 with FIT vs. 2235 with ng mt-sDNA), more advanced precancerous lesions, and more CRC at earlier stages. The cost of screening per patient screened was USD 801 for ng mt-sDNA and USD 124 for FIT. Follow-up colonoscopy cost was USD 149 million with ng mt-sDNA versus USD 22 million with FIT, whereas CRC treatment costs were lower for ng mt-sDNA (USD 1423 million versus USD 1474 million, respectively). When accounting for both direct and CRC averted costs, the total cost of screening and treatment was USD 1383 million with ng mt-sDNA versus USD 1427 million with FIT. Conclusions: Higher screening costs with ng mt-sDNA versus FIT are counterbalanced by savings realized from enhanced CRC prevention and earlier detection due to the superior test performance and better adherence with ng mt-sDNA.

Background/Objectives: Surveillance for anal squamous cell carcinoma (ASCC) recurrence relies on clinical examination and imaging. Post-treatment edema, fibrosis, and inflammation can result in clinically indeterminate findings (CIFs) that delay diagnosis and increase patient and system burden. Circulating tumor tissue-modified viral (TTMV)-HPV DNA offers a biologically specific, noninvasive biomarker that may clarify equivocal assessments. Methods: In this multi-center retrospective study, 233 patients with HPV-associated ASCC were evaluated, including 185 with ≥1 post-treatment TTMV-HPV DNA test. CIFs were defined as exams or imaging results not definitively positive or negative for disease, and were paired with subsequent TTMV-HPV DNA tests. Concordance was defined by prespecified follow-up windows comparing TTMV-HPV DNA results with subsequent clinical outcomes. Results: Ninety patients (39%) experienced 214 CIFs, arising from exams (46%, 98) or imaging (54%, 116). Indeterminate rates by assessment were 7% for exams, 17% for imaging, and 1.3% for TTMV-HPV DNA testing. Overall, 52 CIF/TTMV-HPV DNA pairs were eligible for analysis, and TTMV-HPV DNA resolved disease status accurately for 48/52 (92%, 95% CI: 81.5–97.9). Negative tests predicted cancer-free status for 37/41 CIFs (90%), while 100% of positive tests (11/11) were concordant with clinically confirmed recurrence. In 73% of positive cases (8/11), TTMV-HPV DNA was the first indication of recurrence (median lead-time 29 days; IQR 25–147). Conclusions: TTMV-HPV DNA testing reliably clarifies clinically indeterminate findings during ASCC surveillance, demonstrating high accuracy (92%) and earlier detection of recurrence. These data support integration into post-treatment management to reduce diagnostic uncertainty and guide timely care.

Study Design. Modified Delphi consensus process Objective. To establish a standardized, consensus-driven definition for postoperative spine infection (PSI) for research, diagnosis, and management. Summary of Background Data. Postoperative infection is a devastating and common complication following spine surgery. Rates of PSI in the literature range from 0% to over 20% depending on surgical indication and invasiveness. Despite the implications of PSI, there is no universally accepted diagnostic standard or consensus definition for infection following spine surgery, hindering research and treatment of this challenging clinical condition. Methods. A multispecialty workgroup convened by the Musculoskeletal Infection Society (MSIS) conducted a systematic literature review. Using data from the systematic review, an expert panel completed a modified Delphi process to achieve consensus. The panel included nine fellowship-trained, board-certified physicians with expertise in infectious diseases, orthopaedic and neurologic spine surgery, and musculoskeletal radiology. Iterative rounds focused on criterion selection, importance ranking, categorical grouping, and final constellation development for PSI diagnosis. Consensus was defined as a 2/3 majority, with a target of 100%. Results. The workgroup achieved 100% consensus on a new PSI definition, which incorporates six clinical domains: wound features, microbiology, imaging, inflammatory biomarkers, intraoperative findings, and histology. A single microbiological stand-alone criterion—identification of a phenotypically indistinguishable organism from two or more deep operative site specimens—was established as pathognomonic for postoperative spine infection. Additional primary and secondary supporting criteria were defined, with specific combinations required to classify cases as definite or probable PSI. Final definitions were ratified by the boards of MSIS and the European Bone & Joint Infection Society (EBJIS) Conclusion. This consensus-based definition provides a standardized framework for diagnosing postoperative spine infection, facilitating research and clinical management. The criteria balance sensitivity and specificity across diverse clinical scenarios and represent the first multispecialty, society-endorsed definition for PSI.

Background: Patients with abdominal aortic aneurysms (AAA) often have comorbidities that make them poor candidates for open surgical repair (OSR). Endovascular aortic aneurysm repair (EVAR) circumvents the morbidity associated with OSR of AAA. Candidacy for EVAR is impacted by multiple factors, including the quality and size of vascular access vessels and involvement of visceral arteries, as seen in paravisceral abdominal aortic aneurysms (PVAAA). Additional challenges, such as obesity, can complicate vascular access during EVAR. PVAAA may be better suited for fenestrated EVAR (FEVAR) using a physician-modified endograft (PMEG). Materials and Methods: In this report, we describe the case of a 64-year-old female patient with a complex PVAAA in the setting of significant aortoiliac occlusive disease (AIOD) with intermittent claudication. We describe a successful staged FEVAR technique in which vascular access challenges were first addressed by recanalization of the iliac system with femoral conduit (FC) creation, followed by successful FEVAR using bilateral FC for deployment of the PMEG. Conclusion: Patients with PAAA have unique and complex pathology that are clinically challenging to address. It is not uncommon that these patients also have co-morbid conditions that make them less-than ideal candidates for open repair. Patients may also have other conditions such as AIOD which make EVAR complex. Staged approach with iliac recanalization and femoral conduit creation followed by FEVAR with PMEG is an effective treatment option for high-risk complex patients and can help avoid common complications such as groin infection and delay in FEVAR which may result in a catastrophic event such as interval AAA rupture.

Abstract Objective Constipation is a common gastrointestinal concern in children, often prompting screening for celiac disease (CeD), particularly in refractory cases. However, limited data exist on the clinical course of constipation in children with CeD following treatment with a gluten‐free diet. Methods We reviewed electronic health records from the Mayo Clinic between 1994 and 2024 for children (<18 years) who presented with constipation and then diagnosed with CeD. Children were classified into groups based on the symptoms at the time of presentation. Constipation outcome was considered resolved by the documentation of improvement on a gluten‐free diet without ongoing laxative use, while unresolved cases required continued laxative therapy. A sub‐analysis was performed for children with encopresis. Results We identified 248 children with CeD, 177 (71%) had biopsy‐confirmed CeD, and 56 (23%) were diagnosed with serology‐only criteria. The mean age at diagnosis was 7.8 years (±4.98), with most being White (99%) and female (68%). Constipation was the main presenting symptom, but most children also exhibited additional symptoms at the time of diagnosis. Over an average of 5.3 years (±2.1) of follow‐up, constipation was resolved in 32% of children, while 68% required ongoing laxatives, in the subgroup of children with encopresis, only 29% had documented resolution. Tissue transglutaminase antibody normalization was slower in children with unresolved constipation. Trends were observed toward a higher prevalence of anxiety, depression (42% vs. 30%) and thyroid disorders (13% vs. 9%) in the unresolved group. We also noticed a trend to a higher likelihood of constipation resolution in children with type 1 diabetes mellitus (14% vs. 4%), but none reached statistical significance. Conclusion Many children with CeD continue to experience persistent unresolved constipation despite adherence to a gluten‐free diet. Comorbidities are associated with a lower rate of constipation resolution.

Introduction: Adult hepatitis B vaccination coverage in the United States remains low despite updated national recommendations supporting universal adult immunization. Many adults remain unaware of their susceptibility to hepatitis B virus (HBV) infection. This quality improvement (QI) study evaluated a stepwise “Offer–Offer–Test–Vaccinate” workflow designed to improve hepatitis B vaccine uptake among vaccine-hesitant adults in a primary care setting. Methods: This retrospective QI study was conducted at a single primary care clinic in Rochester, Minnesota. Adult patients (≥19 years) seen between October 1, 2022, and October 1, 2024, were routinely offered hepatitis B vaccination during rooming. Two participating physicians implemented an enhanced workflow in which patients who declined vaccination were counseled and then offered serologic screening using the 2023 CDC-recommended triple panel (HBsAg, anti-HBs, and total anti-HBc). Patients with negative serology were notified via the patient portal and reoffered vaccination along with a link to the CDC Vaccine Information Statement. The primary endpoint was initiation of the hepatitis B vaccine series following receipt of negative serology results. Preliminary series-completion data through October 1, 2025, were also assessed. Results: Among 501 eligible adults, 104 (20.8%) accepted vaccination at the initial offer. Of the 397 who declined, 106 (26.7%) agreed to serologic testing; 91 (85.8%) were seronegative. Among seronegative patients, 41 (45.1%) subsequently accepted vaccination. Integrating serologic testing increased overall vaccination uptake from 20.8% to 28.9%, an absolute increase of 8.2 percentage points and a relative increase of 39.4%. Approximately 2.6 serologic tests were required to generate 1 additional first-dose vaccination. Preliminary series-completion rates were 74.0% among those vaccinated at the initial offer and 87.8% among those vaccinated following serologic testing. Discussion: In this primary care setting, incorporating serologic testing into the workflow was associated with modestly improved hepatitis B vaccination uptake among vaccine-hesitant adults. Serologic results may help re-engage patients by providing personalized immunity information. Implementation considerations include staffing needs, laboratory access, patient portal engagement, and insurance coverage. Conclusion: The “Offer–Offer–Test–Vaccinate” strategy was associated with a modest increase in hepatitis B vaccine uptake among hesitant adults. This EMR-integrated, stepwise approach may offer a feasible and scalable strategy to reduce missed vaccination opportunities and support HBV prevention in adult primary care.

Abstract Background & Aims: Alcohol is a leading global risk factor, over 200 diseases and injuries, but policy adoption varies. We updated the Alcohol Preparedness Index (API) to track global and regional policy trends from 2010–2019. Methods: Using WHO GISAH data for 141 countries, we scored five domains (policy frameworks; production/pricing/taxation; marketing/availability; drink-driving; monitoring/surveillance). The primary endpoint was % change in API. Trends were summarized by WHO region and correlates tested in adjusted regression. Results: In 2019, <25% of countries had a strong national alcohol plan and 40% had none. Production/pricing/taxation increased most (27%→78%); monitoring/surveillance reached strong levels in 47.5%. Median API increased from 57.0 [39.0–82.0] (2010) to 74.8 [58.3–83.5] (2016) and stayed above baseline in 2019 (67.8 [51.2–83.8]). Larger population predicted less API gain (β=−19.1, p<0.05); HDI and per-capita consumption did not. Conclusions: Global alcohol policy preparedness increased from 2010–2019.

Background: Adequate medication dosing in the intensive care unit (ICU) remains challenging due to rapid physiological variability, narrow therapeutic windows, and evolving standards of care. Artificial intelligence (AI) offers promise for personalized dosing, but its accuracy and clinical utility remain uncertain. Methods: We conducted a systematic scoping review (2011-2025) of PubMed, Embase, and Cochrane, following the Joanna Briggs Institute (JBI) and preferred reporting Items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines (open science framework [OSF]-registered). Eligible studies applied AI methods ( e.g. , machine learning [ML], deep learning [DL], reinforcement learning [RL], and natural language processing [NLP]) to ICU medication dosing or monitoring. Results: Data extraction included study design, drug class, model inputs and outputs, algorithms, validation approach, and reported outcomes. Forty-seven studies met the inclusion criteria, which were mostly retrospective and focused on adult ICU populations over the past five years. Antibiotics and anticoagulants were the most studied classes, with vancomycin being the most common. Models primarily used labs, vitals, and demographics, with limited incorporation of time series, pharmacokinetic data, or clinical notes. Outputs spanned dose prediction, serum concentration, risk stratification, adverse events, and decision support. Algorithms evolved from classical ML and ensembles to RL and long short-term memory (LSTM)-based models, although a few advanced beyond retrospective validation. Discussion: AI systems showed strong predictive performance and some clinical benefits ( e.g. , heparin target attainment, sedative reduction, and adverse event detection), but the field remains fragmented and clinically immature. Newer architectures and systematic explainability are lacking, and generalizability is uncertain. AI-enabled dosing holds promise for precision pharmacotherapy in critical illness but requires prospective multicenter validation, inclusion of population pharmacokinetic (PK) parameters, diverse cohorts, explainable design, and clinical integration.