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Artificial intelligence prediction algorithms for refractive error onset and progression in children and adolescents: A systematic review and meta-analysis.

This systematic review and meta-analysis evaluates the performance of artificial intelligence (AI)-based models for predicting the onset and progression of refractive error (RE) in children and adolescents and quantitatively synthesizes their prediction accuracy. The study was preregistered in the PROSPERO database (CRD420251075160) and conducted in accordance with PRISMA guidelines. MEDLINE (via PubMed), Web of Science and Scopus were searched without time restrictions. Eligible studies involved children and adolescents (≤18 years) and applied AI-based technologies to predict the onset or progression of RE, using non-image-based input data. To evaluate the prediction accuracy of the included studies, a meta-analysis was performed. Quality assessment was conducted using the PROBAST+AI tool. Sixteen studies were included in the analysis. All studies evaluated myopia, two additionally addressed hyperopia and none focused on astigmatism. Most models predicted continuous SE values across the refractive spectrum, whereas a subset focused on classification-based outcomes, such as myopia onset or progression. Included studies used diverse input data, including age, gender and parental myopia. Eight studies comprising 18 independent evaluations were included in the meta-analysis. The pooled root-mean-squared error (RMSE) for spherical equivalent prediction was 0.50 D (95% confidence intervals: 0.37-0.62). Between-study heterogeneity was substantial (τ2 = 0.0695, I2 = 100%), indicating major differences across models. This high heterogeneity reflects substantial differences in study populations, prediction targets and horizons, model architectures and validation strategies across included studies, and therefore, the pooled estimate should be interpreted as an average across diverse settings rather than a directly generalizable performance measure. Leave-one-out sensitivity analyses demonstrated that no single study materially influenced the pooled effect. Across all iterations, RMSE estimates ranged narrowly from 0.48 to 0.52 D, with confidence intervals overlapping the main analysis. The studies' quality assessment showed mostly low concern, apart from the analysis domain, in which 43.8% of studies were rated high risk. AI models using non-image-based clinical data demonstrate moderate accuracy for predicting paediatric RE onset and progression. However, substantial methodological heterogeneity and limited external validation indicate that standardized development and validation frameworks are required before clinical implementation.

Evidence boundary: 异质性很高，汇总指标不是个体预测承诺。

Source: PMID:42507990

LATEST VISIBLE BATCH

Performance of multimodal large language models in interpreting lateral cephalometric superimpositions: A comparative observer-performance study.

Observation date: 2026-07-28. The primary-source endpoint did not yield a verifiable new batch in this run; this issue uses the latest visible record from 2026-07-27, not a publication-date claim for the observation date.

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Multimodal large language models (LLMs) can generate free-text interpretations of clinical images, but their performance on orthodontic cephalometric superimpositions is unknown. This study compared zero-shot interpretations from three LLMs with those of a second-year orthodontic resident. Ninety lateral cephalometric superimposition images from a private orthodontic practice were analyzed, including 30 nongrowing, 30 growing, and 30 orthognathic cases. Each image included overall maxillary regional, and mandibular regional superimpositions. ChatGPT 5.5, Gemini 3.1 Pro, Claude Opus 4.8, and the resident interpreted the same images using the same prompt, with no case context provided. Two senior orthodontists scored each interpretation against adjudicated reference interpretations using a 16-item rubric, yielding total scores from 0 to 32 and four domain scores. Friedman tests compared methods; Wilcoxon signed-rank tests with Holm adjustment compared each LLM with the resident. Total scores differed significantly among methods (Friedman P<0.001; Kendall W=0.592). Median total scores were 30.5 (interquartile range [IQR]: 28-32) for the resident, 17 (IQR: 14-22) for ChatGPT, 12 (IQR: 9-16) for Gemini, and 9.5 (IQR: 4.25-14) for Claude. All LLMs scored significantly lower than the resident overall, by domain, and within each case type (adjusted P<0.001). In zero-shot cephalometric superimposition interpretation, all tested multimodal LLMs performed substantially below a second-year orthodontic resident. These models should not be used as stand-alone interpreters without expert review.

Evidence boundary: 单中心、无病例背景的观察不能外推为全部影像判读或临床决策能力。

Source: PMID:42508094

Observation

2026-07-28

Asia/Shanghai

Latest visible source

2026-07-27

not a 28 July publication claim

Selected

primary-source and boundary screen

Same-day batch

unverified

a failed endpoint is not a zero-result claim

METHOD / NEXT

Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems.

Pre-deployment validation is commonly used to establish the safety and effectiveness of clinical artificial intelligence systems, but acceptable validation performance does not guarantee stable behavior after deployment into routine clinical workflows. We conducted a longitudinal retrospective observational study of four clinically deployed AI systems operating across distinct clinical domains and workflows within a large healthcare organization. Using routinely collected clinical data, outcome labels, and operational telemetry, we compared validation-era performance with post-deployment behavior over extended observation periods. Analyses focused on temporal patterns of discrimination, calibration, data availability, latency, and workflow-related signals, with particular attention to label-dependent and label-independent monitoring. Across all systems, validation-era performance did not persist as a stable operational property after deployment. Calibration drift emerged consistently and often preceded detectable changes in discrimination. Workflow-associated changes in data availability and timing were more strongly and consistently associated with degradation than population-level indicators. Label-independent operational signals, including input missingness and data latency, provided early indication of emerging fragility, whereas outcome-based monitoring was delayed by label latency and documentation processes. These findings suggest that post-deployment fragility can be a structural property of clinical AI systems embedded in evolving workflows. Effective governance therefore requires lifecycle-oriented monitoring strategies that combine calibration reassessment with operational telemetry throughout deployment.

Evidence boundary: 单一医疗机构观察支持监测需求，不给出产品通用失效概率。

Source: PMID:42507719

Performance of multimodal large language models in interpreting lateral cephalometric superimpositions: A comparative observer-performance study.

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