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EviGround-CT proposes sentence-to-volume grounding to verify LLM-generated chest CT reports by predicting 3D evidence masks for each positive finding sentence, improving evidence-supported precision from 0.263 to 0.365 over category conditioning. The work is evaluated on CT-RATE and a ReXGroundingCT subset to detect unsupported findings.
本文提出 Incremental Open-Ended Deep Research（Incremental-OEDR）研究设定与 Structured Harness 框架，将研究报告视为可随新信息持续演化的结构化状态，在保持报告质量的同时显著降低研究成本（token 消耗减少 33%，搜索调用减少 61%）。
The open-source tool last30days-skill surged to GitHub Trending's top spot with 7,000+ stars in a week (13,000 total), scanning 10 major platforms—including X, Reddit, YouTube, and Polymarket—to condense the past 30 days of buzz into a single cross-verified report.
This study evaluates Jev, a System One model, for measuring factual differences in AI-generated radiology reports, demonstrating its effectiveness and efficiency compared to other evaluation methods.
A solo founder reduced client complaints on automated reports by moving prose writing from an AI agent to a dedicated content generator, focusing the agent on reasoning and structured findings.
The author found that simplifying an AI report generator agent to focus on data assembly and fixed templates, rather than analysis, enhanced its usability and trust, highlighting the importance of human judgment in automated reporting.
Baidu's Kooko AI reached over 10 million international users in its first year, offering an integrated AI workspace for streamlined research and professional report generation.
The paper introduces a method using corpus characterization and inverse constitutional fine-tuning to improve the stylistic alignment of AI-generated radiology reports with authentic radiologist writing. This approach achieves significant gains in text alignment metrics, demonstrating effectiveness for style-aware report generation.
This paper proposes a composite objective for cone beam CT report generation that prioritizes factual entailment over lexical overlap, demonstrating that optimizing for lexical metrics harms factual accuracy. It releases a dataset and code, and presents a system that generates constrained clinical reports under polarity, laterality, and tooth level consistency constraints.
Keenable SELECT is an MCP server that enables AI agents to execute SQL queries on live web data, using semantic operators for efficient search and extraction, and generating HTML reports.
Lieflat Charts is an open-source tool that has reached 2k GitHub stars, enabling AI agents to create aesthetically pleasing charts with over 60 types using HTML for rich animations and report generation.
Building an AI report generator for a client revealed that most effort went into data normalization and templating, with the model being a minor component, underscoring the primacy of data pipelines over generation.
The author built an AI agent for market research that automates report assembly but still requires human judgment for contextual decisions, highlighting the balance between AI automation and human expertise in knowledge work.
RadFusion is a framework that adds threshold controllability to radiology report generation by fusing a multi-label classifier with a VQA-based generator and an LLM rewrite step, enabling sensitivity-specificity trade-offs and ROC-based validation. Experiments on MIMIC-CXR show improved diagnostic accuracy and clinically adaptable report behavior.
A developer shares lessons from running an AI report generator in production, arguing that data quality and validation matter far more than the model's writing ability, since fluent but incorrect reports are dangerous.
Introduces MCTS-Report, a Monte Carlo Tree Search framework for generating multimodal reports from tabular data, along with the MMRBench benchmark. It outperforms strong baselines across structural completeness, numerical accuracy, chart-text alignment, and insight novelty.
This paper highlights that VLMs for chest x-ray report generation can score well on benchmarks while erasing clinically meaningful terms and introducing biased language, and proposes a framework to measure these failures.
This paper adapts a diffusion language model for interactive radiology report drafting, showing it matches autoregressive models in accuracy while offering unique infill capabilities that allow radiologists to fix report fragments and have the model fill in the text between them.
A tool that automates research and report generation by aggregating information from multiple sources, likely using AI.
This paper introduces transition-aware best-of-N sampling, a training-free method for generating longitudinal chest X-ray reports by encoding changes between prior and current examinations using set-to-set distance metrics.