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AI-generated content
Also known as: Artificial intelligence–generated content
Facts (17)
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On Hallucinations in Artificial Intelligence–Generated Content ... jnm.snmjournals.org 14 facts
claimHallucinations in artificial intelligence–generated content (AIGC) for nuclear medicine imaging (NMI) are defined as the generation of realistic yet factually incorrect content that can misrepresent anatomic and functional information.
referenceFarquhar et al. define confabulations as a subset of hallucinations where artificial intelligence-generated content is both incorrect and arbitrary, meaning the model outputs fluctuate unpredictably under identical inputs due to irrelevant factors like random seed variations.
claimThe DREAM report provides a comprehensive perspective on hallucinations in artificial intelligence–generated content (AIGC) for nuclear medicine imaging (NMI).
claimFactual hallucinations are defined as artificial intelligence-generated content that contradicts verifiable knowledge, while faithfulness hallucinations are defined as content that violates instructions or source input.
claimHallucinations in Artificial Intelligence–Generated Content (AIGC) for Nuclear Medicine Imaging (NMI) are typically subtle and deceptive, manifesting as added small abnormalities or realistic-looking lesions that do not exist in reality.
perspectiveThe authors of the paper 'On Hallucinations in Artificial Intelligence–Generated Content' focus specifically on artificial intelligence-generated content in nuclear medicine imaging (NMI), excluding errors or artifacts introduced by traditional imaging workflows.
claimArtificial intelligence–generated content (AIGC) provides cost-effective software solutions for nuclear medicine imaging (NMI) tasks, including image enhancement, motion correction, and attenuation correction.
claimIn natural language processing, hallucinations are typically defined as artificial intelligence-generated content that is inconsistent with given targets.
claimThe medical imaging community currently lacks a domain-specific and systematic analysis of hallucinations in artificial intelligence–generated content (AIGC), unlike the natural language processing community which has recently explored this topic.
claimHallucinations in artificial intelligence–generated content (AIGC) used in nuclear medicine imaging (NMI) can lead to cascading clinical errors, including misdiagnosis, mistreatment, unnecessary interventions, medication errors, and ethical or legal concerns.
claimArtificial Intelligence-Generated Content (AIGC) in medical imaging can appear visually accurate but may contain hallucinations when compared against reference CT attenuation correction (AC) images.
formulaMost artificial intelligence-generated content (AIGC) applications in nuclear medicine imaging (NMI) can be formulated as image-to-image estimation tasks, where the objective is to learn a mapping function from a source domain S to a target domain T, denoted as G: S -> T.
claimHallucinations in artificial intelligence-generated content arise when the learned mapping function deviates from the true underlying mapping G.
claimMost artificial intelligence-generated content applications in nuclear medicine imaging operate as image-to-image translation tasks, where implausible large-scale errors like the addition of organs or major structures are rarely observed.
Cybersecurity Trends and Predictions 2025 From Industry Insiders itprotoday.com 1 fact
perspectiveTK Keanini, the chief technology officer at DNSFilter, predicts that privacy regulations regarding the use of personal likenesses in AI-generated content will emerge, making consent mandatory.
The Complete Guide to Open Source Licenses - FOSSA fossa.com 1 fact
claimTraditional open source licenses create challenges for AI and machine learning, specifically regarding whether using open source code to train models constitutes 'use' under licenses, whether AI-generated content inherits license obligations, and the emergence of new AI-specific licenses.
KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented ... arxiv.org Mar 18, 2025 1 fact
referencePenghao Zhao, Hailin Zhang, Qinhan Yu, Zhengren Wang, Yunteng Geng, Fangcheng Fu, Ling Yang, Wentao Zhang, and Bin Cui authored the paper 'Retrieval-augmented generation for ai-generated content: A survey', published as arXiv preprint arXiv:2402.19473 in 2024.