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At least 19 recordsLinked to original sources
PubMed2025

Triage and workflow optimization with artificial intelligence in pediatric imaging.

Artificial intelligence (AI) is being increasingly utilized in various aspects by the radiology department. With an ever-increasing burden on the healthcare system, particularly in emergency units, the need to incorporate AI in patient triage and workflow optimization cannot be overstated. Machine learning (ML)-based algorithms form the core of AI-based software, aiding healthcare professionals at nearly every step in delivering appropriate patient care. Regarding the radiology section of the hospital, AI-based algorithms have proven exceptionally useful in assisting radiologists and technicians with image acquisition. From accurate clinical referrals to scheduling computed tomography/magnetic resonance imaging scan appointments, from ensuring the lowest radiation exposure to offering timely follow-up reminders, ML-based software has indeed revolutionized the concept of modern image acquisition, especially in the pediatric radiology section. Although the implementation of these algorithms is swift, several technical challenges and the limited availability of pediatric datasets preclude their widespread use. The utility of multimodal pediatric datasets, which combine imaging, genomics, and clinical data, for comprehensive AI triage models can help AI systems evolve toward greater adaptability and integration, resulting in enhanced efficiency, reduced turnaround times, and improved patient outcomes in pediatric radiology departments in the future. In this article, we highlight and review the utility of AI and machine learning-based algorithms in efficiently aiding triage and streamlining the workflow in the pediatric radiology section, thereby ensuring an overall improvement in the departmental workflow.

Triage↗
PubMed2026

The potential of clustering methods for pre-test triage in sleep medicine: A systematic review.

Sleep disorders exhibit substantial heterogeneity, and traditional classifications may not fully capture clinically relevant subtypes. Clustering techniques can identify patient subgroups that improve phenotypic characterization and may support personalized management. This systematic review evaluated the application of clustering in sleep medicine, with particular focus on its potential use as a pre-test triage tool prior to formal sleep testing. PubMed/MEDLINE, Embase, Web of Science, and Scopus were searched to February 2025. Eligible studies applied clustering to classify sleep disorders in adults. Two reviewers independently conducted screening, data extraction, and risk-of-bias assessment using QUADAS-2. The protocol was registered on PROSPERO. Fifty-one studies (1983-2025) were included, predominantly focused on obstructive sleep apnea (OSA) (n = 38, 74%). Hierarchical clustering (n = 20) and K-means clustering (n = 14) were the most frequently used techniques. Internal validation was reported in only 18% of studies, and external validation was reported in only 1 study. Seven studies relied exclusively on baseline clinical, demographic, or questionnaire data, representing pre-test scenarios, whereas most incorporated polysomnography-derived variables, limiting their applicability to early clinical stratification. Hierarchical clustering was the most commonly applied method; however, the overall lack of validation limits confidence in the robustness and clinical applicability of identified phenotypes. The potential role of clustering as a pre-test triage strategy remains largely unexplored, as most studies focused on post-diagnostic phenotyping and were affected by incorporation bias. Future research should prioritize pre-test clinical variables, rigorously validate internally and externally, and adopt standardized methodological and reporting practices to facilitate clinical translation.

Humans↗
PubMed1976

Call for help: an algorithm for burn assessment, triage, and acute care.

Because of the Medical College of Georgia Burn Treatment Center frequently received patients with extensive burns who had been badly managed locally, we devised an Algorithm to provide health care professionals a logical method for assessment, triage and obtaining help. Limited experience suggests that the Algorithm does, in fact, accomplish these ends but that continued evaluation of its efficacy is necessary.

Aged↗
PubMed1977

Mobile triage team in a community disaster plan.

Experience has shown poor predisaster planning, inadequate communication and the absence of an on-scene commander to be common and recurring problems during disaster rescue efforts. A mobile onscene triage team (MOTT) operating in Sacramento has demonstrated the following advantages: immediate access; mobility; coordinated evacuation, treatment, and disposition of mass casualty victims; control of facility overload, and appropriate initial disposition to definitive care facilities. The advantages realized with this approach arise from greater community awareness and participation in a coordinated plan for medical care in disasters.

California↗
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↗
PubMed1979

Cost-effectiveness evaluation of a home visiting triage program for family planning in Turkey.

Graduate Turkish midwives were trained in triage rules for determining family planning home visit frequency based on risk of couples. In a sample of 542 couples followed for six months, modern contraceptive use increased 22 per cent among high-risk and about 15 per cent among moderate- and low-risk couples. After making assumptions about the fecundity, contraceptive success, and pregnancy complications, the estimated average cost per complication averted was $61 for high-risk, $177 for moderate-risk and $526 for low-risk couples.

Adolescent↗
PubMed1978

Emergency Department treatment, triage and transfer protocols for the burn patient.

Emergency department treatment, triage and transfer protocols for patients with major thermal injury have been devised by the three burn centers in Virginia. A burn nurse educator has presented these guidelines to the emergency departments of Virginia. The development of these protocols has considerably improved the immediate care of the victims of thermal injury who are transferred to the burn centers in the Commonwealth of Virginia.

Adolescent↗
PubMed2026

Somatic likelihood tiering: an interpretable post-calling triage protocol for tumor-only whole-exome variant review.

Tumor-only whole-exome sequencing (WES) is used when matched normal tissue is unavailable, but one sample can produce thousands of variants. Somatic likelihood tiering (SLT) is an interpretable post-calling protocol that ranks Mutect2 calls into four review-priority tiers using population-frequency, germline-quality, cancer-knowledge, PureCN posterior, and clonal-hematopoiesis evidence. Layer 2 distinguishes common, rare-callable, and unevaluable gnomAD states; missing or unmatchable gnomAD evidence is not positive rarity evidence. On the SEQC2 HCC1395 benchmark, the callability-aware SLT-A row contained 101 calls, 78 truth variants, 77.2% PPV (95% Wilson confidence interval 68.1%-84.3%), and a Number Needed to Review (NNR) of 1.29 (1.19-1.47). The conservative SLT-C catchment retained 352 of 455 truth variants (77.4%, 73.3%-81.0%) and all tiers together retained 430 of 455 truth variants. SNV performance is the primary calibration frame: SLT-C retained 341 of 439 SNV truth variants, whereas indel results were exploratory because only 16 truth indels were available. Clinical cohorts are reported as recall and concordance versus partially dependent matched-normal Mutect2 references, not independent clinical sensitivity. Patient-level bootstrap intervals were principal: HdM-BLCA-1 SLT-A recall was 18.2% (14.0%-23.5%), and LUAD-TW SLT-A recall was 49.1% (26.6%-63.3%) among 32 evaluable patients. The HdM-BLCA-1 median SLT-A queue remained 1277 variants per patient, so SLT reduces first-pass candidate counts but does not measure review time or eliminate FFPE candidate-count burden. SLT provides an auditable tumor-only WES review queue, not a substitute for matched-normal sequencing, independent orthogonal validation, or definitive somatic classification.

Humans↗