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PubMed2026

Artificial Intelligence Cannot Replace Peer Reviewers but May Help Editors Triage: A Comparative Analysis of a Large Language Model and Human Reviewer Recommendations at the American Journal of Sports Medicine.

BACKGROUND: The peer review system faces increasing strain from rising manuscript volumes, reviewer fatigue, and well-documented interreviewer disagreement. Large language models (LLMs) have shown potential to support the peer review process, but their ability to replicate editorial decisions at high-impact medical journals and their utility as manuscript screening tools remain unknown. PURPOSE: To compare the agreement between an LLM and the final editorial decision on manuscripts submitted to the American Journal of Sports Medicine and to evaluate the potential of LLMs as a manuscript screening tool. STUDY DESIGN: Cross-sectional agreement study. METHODS: Fifty-four manuscripts randomly selected from submissions to the American Journal of Sports Medicine (September 2024-October 2024) were reviewed by a locally deployed LLM (Ministral 3 14B; Mistral AI) using a standardized prompt. The artificial intelligence (AI) produced a categorical recommendation (reject, cascade, revision, or accept) and a numerical score (0-100) for each manuscript. Agreement with the final editorial decision was assessed by Cohen kappa (4-category model) for pooled human reviewers (n = 139 reviews) and the AI (n = 54). Screening performance was evaluated by positive predictive value (PPV), sensitivity, and specificity. RESULTS: Pooled human reviewers demonstrated fair agreement with the final decision (&#x3ba; = 0.181 [P < .001]; 42.4% agreement), while the AI demonstrated slight, nonsignificant agreement (&#x3ba; = 0.126 [P = .099]; 37.0% agreement). The AI recommended revision for 61.1% of manuscripts, of which 72.7% were ultimately rejected or cascaded, demonstrating systematic "revision bias." When the AI recommended rejection, 54.5% of those manuscripts were ultimately rejected and 27.3% were cascaded; when the AI recommended cascade, 50% were rejected and 50% were cascaded. However, when the AI recommended rejection or cascade (n = 21), 90.5% received a final decision of rejection or cascade (PPV, 90.5%; specificity, 81.8%). Manuscripts with an AI score <70 were rejected or cascaded 88.0% of the time (PPV, 88.0%). CONCLUSION: AI cannot replicate the nuanced judgment of human peer reviewers at a high-impact sports medicine journal. When AI recommended rejection or cascade, 90.5% of manuscripts received that final decision (descriptive PPV, 90.5%; 95% CI, 71.1%-97.3%), suggesting potential utility as an exploratory first-pass screening tool warranting further validation in larger cohorts. However, AI could not reliably distinguish manuscripts destined for outright rejection from those that would be cascaded to a sister journal-an important limitation for editorial triage applications.

Sports Medicine↗
PubMed2026

Retraction of the landmark glyphosate safety publication by Williams, Kroes and Munro (2000) should be reversed.

The decision by the co-Editor-in-Chief of Regulatory Toxicology and Pharmacology, Prof. Martin van den Berg, to retract the 2000 review article by Williams, Kroes, and Munro has elicited widespread criticism within the scientific community. Issued in late 2025, the retraction decision cites procedural concerns including potential ghostwriting, undisclosed conflicts of interest, and omission of certain unpublished studies, invoking Committee on Publication Ethics guidelines despite lacking evidence of fraud or scientific flaws. This editorial argues that the retraction decision involves editorial overreach and misapplication of the guidelines. The alleged omissions stemmed from proprietary data access limitations that were disclosed in the original paper. Subsequent reviews by several independent expert panels and regulatory authorities with access to all glyphosate data, including the studies cited by the retracting editor, reached similar conclusions. Claims of ghostwriting were previously investigated and found lacking, including a declaration by EFSA as to the clarity of the conflict disclosures. The retraction's timing, reliance on litigation documents, and apparent biases that were not disclosed in the retraction notice raise questions of ideological interference. Absent substantive rebuttals based on scientific merit rather than speculative claims of inappropriate authorship and data access, this retraction decision sets a dangerous precedent for retroactive censorship, potentially chilling beneficial industry-academic collaborations and eroding trust in the integrity of scientific publishing. With the strongest conviction, we assert that retracting a paper without scientific flaws isn't protection-it is censorship. We therefore call for the immediate reversal of this flawed and unjustified retraction to preserve trust in peer-reviewed literature.

COPE↗
PubMed2025

Modeling unknowns: A vision for uncertainty-aware machine learning in healthcare.

The integration of machine learning (ML) into healthcare is accelerating, driven by the proliferation of biomedical data and the promise of data-driven clinical support. A key challenge in this context is managing the pervasive uncertainty inherent in medical reasoning and decision-making. Despite its recognized importance, uncertainty is often underrepresented in the design and evaluation of clinical AI systems. Here we report an editorial overview of a special issue dedicated to uncertainty modeling in medical AI, which gathers theoretical, methodological, and practical contributions addressing this critical gap. Across these works, authors reveal that fewer than 4% of studies address uncertainty explicitly, and propose alternative design principles-such as optimizing for clinical net benefit or embedding explainability with confidence estimates. Notable contributions include the RelAI system for real-time prediction reliability, empirical findings on how uncertainty communication shapes clinical interpretation, and benchmarks for out-of-distribution detection in tabular data. Furthermore, this issue highlights the use of causal reasoning and anomaly detection to enhance system robustness and accountability. Together, these studies argue that representing, communicating, and operationalizing uncertainty are essential not only for clinical safety but also for building trust in AI-driven care. This special issue thus repositions uncertainty from a limitation to a foundational asset in the responsible deployment of ML in healthcare.

Machine Learning↗
PubMed2025

A Qualitative Study of the Roles and Responsibilities of Academic and Journalistic Publishing in Social and Behavioral Genomics.

The conduct and translation of scientific research is shaped by academic and journalistic publishing. Academic journals issue editorial guidelines and policies that inform how researchers shape and present their studies. Journalists select and report on academic studies for public audiences. Despite the potential importance of journal editors and journalists in the scientific process, little has been done to examine how these groups think about their roles and responsibilities-especially when it comes to ethically sensitive scientific domains like social and behavioral genomics (SBG): the study of whether and how genetic differences between individuals correlate with differences in behaviors such as aggression and outcomes such as educational attainment. To begin filling this gap, we conducted semi-structured interviews with editors working at academic journals that publish SBG research (n&#x2009;=&#x2009;10) and journalists who have reported on SBG studies (n&#x2009;=&#x2009;13). Journal editors largely saw themselves as mediators between authors and peer reviewers who help to shepherd along research. Journalists frequently described themselves as translators of science for wide audiences; at times they also saw themselves as interrogators of science. While both groups considered SBG especially ethically sensitive and prone to risks such as misinterpretation, many expressed that systematic ethical review processes and guidelines for SBG are lacking. Further, many deferred the ethical responsibility to minimize risks associated with SBG to others. Our findings highlight the need for more explicit frameworks in academic and journalistic publishing to support the ethically responsible conduct and communication of SBG.

ELSI↗
PubMed2026

What's the meta now? More updates on the problems with systematic reviews.

BACKGROUND: Systematic reviews are intended to provide trustworthy evidence synthesis, yet previous iterations of this living review have identified numerous recurring problems in their conduct and reporting. This article presents the third version and second update of the living systematic review examining issues raised across the academic literature. METHODS: Using consistent eligibility criteria and methods from earlier versions, literature searches were updated to May 2025. Eligible meta-research and editorial articles describing problems with systematic reviews were analyzed to identify emerging themes. Additionally, four basic indicators of methodological quality of the included meta-research were presented across review versions. RESULTS: The update included 209 additional articles. Critically low methodological quality and absence of protocols remained among the most frequently reported issues in systematic reviews across disciplines and journals but notably in evidence underpinning clinical practice guidelines. Spin in abstracts and conflicts of interest continued to be common. Apparent improvements in reporting quality were inconsistent, with modest gains in some full-text reporting but persistent deficiencies in abstracts. Authorship diversity of systematic reviews improved in gender representation but remained geographically concentrated in high-income countries, and primary research included in reviews similarly lacked global representativeness. The issue of misalignment between systematic review evidence bases and global burden of disease bring the total number of problems with systematic reviews to 69. Emerging use of automation and artificial intelligence was variably reported. Descriptive comparison of meta-research articles over the three versions of this living review suggests a greater proportion meeting basic quality indicators in more recent updates. CONCLUSION: Across successive updates, problems with systematic reviews remain widespread and consistent rather than isolated. Incremental reporting improvements coexist with persistent concerns about transparency, bias, and representativeness. Future efforts should prioritize evaluating interventions and aligning research incentives to support genuinely trustworthy evidence synthesis.

Humans↗
PubMed2026

Pharmacogenomics of antipsychotic-induced weight gain: A systematic review.

BACKGROUND: Antipsychotic-induced weight gain (AIWG) is a major clinical concern, affecting approximately 30% of patients. Clinical predictors explain only part of AIWG risk. Genetic and molecular variations are hypothesized to contribute to susceptibility. The purpose of this review is to summarize recent results to identify replicated and novel findings. STUDY DESIGN: Applying PRISMA guidelines, we searched MEDLINE, Embase, and PsycINFO (May 2018-May 2026) for studies on genetic and molecular associations with AIWG, extending our prior review. Reviews, editorials, and conference abstracts were excluded. We extracted study characteristics (design, diagnosis, antipsychotic exposure, sample size, ancestry, genetic variants, and AIWG outcomes) (e.g., &#x2265;7% weight gain, BMI change). RESULTS: Fifty-three studies met inclusion criteria. In candidate gene studies, the most consistently replicated genes associated with AIWG were observed for DRD2, HTR2C, and MC4R. Multiple novel associations were identified by genome-wide association studies (GWAS) (e.g., MAP2K1, ZDBF2, PEPD), polygenic risk scores (PRS) (e.g., body mass index PRS), gene expression (e.g., CYP3A4, EP300), and epigenetic analyses (e.g., cg12034943 at CRTC1). CONCLUSIONS: Polymorphisms in candidate genes related to neurotransmission and appetite regulation continue to be investigated for associations with AIWG, while novel findings have emerged from GWAS, gene expression, and epigenetic studies. Evidence remains inconsistent due to limited replication, methodological variability, sparse ancestry data, and geographical underrepresentation. No single genetic variant is ready for clinical use, and multi-omic and multi-ancestry models are needed to improve prediction and clinical utility.

Humans↗
PubMed2026

Association between packed red blood cell transfusion and clinical deterioration in neonatal necrotizing enterocolitis: a systematic review and meta-analysis.

BACKGROUND: No systematic review has evaluated the existing evidence regarding the association between packed red blood cell (pRBC) transfusion and clinical worsening of necrotizing enterocolitis (NEC) in neonates. This systematic review and meta-analysis was conducted to address this knowledge gap. MATERIALS AND METHODS: We searched the Cochrane Library, EBSCO, Embase, Web of Science, Google Scholar, and PubMed for studies on pRBC transfusion and NEC published before May 10, 2025. Relevant articles were selected through title, abstract, and full-text screening. English-language case-control studies or cohort studies, or randomized controlled trials involving newborns with NEC that compared pRBC transfusion with no transfusion and reported changes in NEC clinical status were included. Review articles, systematic reviews, case reports, editorials, animal studies, duplicate publications, and studies with incomplete data were excluded. RESULTS: Five studies involving 971 neonates with NEC were included. The pooled analysis demonstrated a potential association between pRBC transfusion and clinical deterioration of NEC in neonates (odds ratio: 6.05, 95% confidence interval: 3.02-12.14). CONCLUSIONS: pRBC transfusion was associated with an exacerbation of NEC in neonates. However, these findings should be interpreted cautiously because of the small number of eligible studies included in this meta-analysis, and future large-scale, well-designed studies are needed to confirm the observed association.

Humans↗
PubMed2026

Empirical Meropenem Versus Piperacillin/Tazobactam for Critically Ill Adults With Sepsis: Feasibility of a Randomised Trial.

BACKGROUND: Meropenem and piperacillin/tazobactam are commonly used empirical antibiotics in critically ill adults with sepsis, but whether one is superior to the other is uncertain. METHODS: The Empirical Meropenem versus Piperacillin/Tazobactam for Adult Patients with Sepsis (EMPRESS) trial is an ongoing investigator-initiated, randomised, open-label, adaptive clinical trial with an integrated feasibility phase comparing empirical treatment with meropenem versus piperacillin/tazobactam in critically ill adults with sepsis. The integrated feasibility phase enrolled 200 participants across 10 intensive care units (ICUs) in Denmark between 28 June and 12 December 2025. Five pre-specified feasibility criteria were evaluated; if all feasibility criteria were met, the trial would proceed unaltered, whereas failure to meet one or more criteria would require intervention and re-evaluation. RESULTS: We randomised 200 of 284 screened patients (70.4%). The median age was 70&#x2009;years (interquartile range (IQR): 60-77), 65.5% were males. At randomisation, 80.0% received vasopressors or inotropes, and 43.5% were on invasive mechanical ventilation. Four of five pre-specified feasibility criteria were met: time to completion of the feasibility phase (5.5&#x2009;months vs. threshold <&#x2009;12.0&#x2009;months), recruitment proportion (70.4% vs. threshold &#x2265;&#x2009;50.0%), proportion of participants without consent to the continued collection of data (2.5% vs. threshold <&#x2009;5.0%) and protocol adherence (81.0% vs. threshold &#x2265;&#x2009;75.0%). The proportion of participants with timely primary outcome data availability (30-day mortality) within 45&#x2009;days was 85.5% and below the pre-specified threshold of &#x2265;&#x2009;95.0%. The proportions were low in the first 3&#x2009;months (33.3%, 22.2% and 30.8%, respectively), increasing to 95.8% in the last month of the feasibility phase. All-cause mortality at 30&#x2009;days was 30.5%, and specific serious adverse reactions occurred in 4.0% of participants. CONCLUSIONS: In this integrated feasibility evaluation of the EMPRESS trial comparing empirical meropenem versus piperacillin/tazobactam in critically ill adults with sepsis, four of five pre-specified feasibility criteria were met. The unmet criterion, timely primary outcome data availability, improved substantially during the feasibility phase. We consider the trial feasible and will proceed without modifications. EDITORIAL COMMENT: This feasibility study assessed recruitment, randomised allocation and data collection for the multicentre EMPRESS trial. For adaptive trials on trial platforms, careful interim checking of trial design functions is an important and necessary process. TRIAL REGISTRATION: Clinical Trials Information System EUCT number: 2023-509703-33-00; ClinicalTrials.gov identifier: NCT06184659; Universal Trial Number: U1111-1301-6379.

Humans↗
PubMed2026

Impact of Commercial Artificial Intelligence on Radiologist Reading Time for Pulmonary Nodule Evaluation at Chest CT.

Background Chest CT is a primary method for identifying pulmonary nodules, yet interpreting scans remains time-intensive and demanding. Currently, artificial intelligence (AI) is expected to reduce reading times, but the effect of AI on reporting times in this setting is unknown. Purpose To evaluate the impact of a commercial AI software on radiologists' reading time for pulmonary nodule assessment on chest CT scans within a real-world clinical setting. Materials and Methods This retrospective study included patients who underwent chest CT examinations at a tertiary medical center between September 2021 and May 2024. The study period was divided into pre- and post-AI phases. The primary outcome was radiology reporting time. The association between AI implementation and reporting time was evaluated using a multivariable parametric Weibull shared frailty survival model adjusted for reader function, examination type, patient location, and requesting specialty, with clustering at the radiologist level. Interaction analyses assessed heterogeneity across prespecified subgroups. An exploratory extrapolation estimated projected workforce and financial impact. Results This study included 19&#x2009;433 patients (mean age, 62 years &#xb1; 14.2 [SD]; 21&#x2009;814 men; 39&#x2009;323 chest CT examinations, 19&#x2009;190 pre-AI, and 20&#x2009;133 post-AI). AI implementation was associated with faster report completion (adjusted hazard ratio, 1.17; 95% CI: 1.14, 1.21; P < .001). The adjusted median reporting time decreased from 21.3 minutes pre-AI to 18.2 minutes post-AI (14.6% reduction; P < .001). Heterogeneity was observed across reader function (P < .001), examination type (P = .048), and requesting specialty (P = .03). The largest relative reductions were observed for CT thorax electrocardiogram-gated examinations (-41.1%; P < .001) and thoracic radiologists (-25.0%; P < .001), whereas emergency department examinations showed increased median reporting time (7.1%; P < .001). At institutional scan volumes (approximately 20&#x2009;000-22&#x2009;000 chest CT examinations annually), exploratory modeling suggested an approximate reduction of 0.5 full-time equivalent radiologist workload. Conclusion Implementation of commercial AI-assisted pulmonary nodule assessment on chest CT scans reduced radiologist reporting time in a real-world clinical setting. &#xa9; The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Iwasawa in this issue.

Humans↗
PubMed2026

Challenges of Using Circulating Tumour DNA: Insights from Advanced Prostate Cancer.

Precision oncology relies on integrating tumour fraction, variant allele frequency, clonal haematopoiesis of indeterminate potential assessment, pathogenicity, and clinical context into next-generation sequencing interpretation, enabling biologically informed and clinically meaningful treatment decisions while reducing the risk of overinterpreting nontumour or nonactionable genomic alterations.

Editorial↗