{
  "abstract": "Artificial intelligence in PET/CT is evolving from narrowly optimized systems toward reusable pretrained representations and multimodal models. PET/CT is a demanding test case because clinically meaningful interpretation must preserve quantitative molecular information while integrating anatomic CT, acquisition metadata, longitudinal examinations, and free-text imaging reports. This focused narrative review synthesizes literature reported from January 2021 through July 2026, spanning PET reconstruction and enhancement, attenuation correction, segmentation, outcome modeling, pretrained representations, vision-language systems, and agentic workflows. We distinguish task-specific models, reusable pretrained representations, promptable models, vision-language systems, generalist foundation-model candidates, and agentic systems according to demonstrated capabilities, while separately considering evaluation benchmarks. Task-specific deep learning has demonstrated promising technical performance in low-count imaging and whole-body tumor analysis, but most evidence remains retrospective and restricted by radiotracer, disease, scanner, institution, or endpoint. Emerging PET-native studies demonstrate label-efficient segmentation, image-text grounding, report generation, and modular tool orchestration; however, they do not yet establish broad cross-tracer generalization or prospective clinical benefit. We therefore propose MOSAIC-PET, an author-developed heuristic six-domain evaluation framework for PET/CT foundation models encompassing Molecular fidelity, Organ and anatomic grounding, Semantic faithfulness, Adaptability, Integration and clinical impact, and Continuous governance. MOSAIC-PET has not undergone Delphi consensus or formal validation and is intended to organize PET-specific evidence requirements rather than function as a validated clinical-readiness tool. The near-term translational priority is the development of modular, auditable systems in which validated quantitative tools and evidence-linked language outputs remain under specialist oversight, with progression from external testing to silent evaluation, assisted-use studies, clinical-impact assessment, and continuous lifecycle monitoring.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.",
    "Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.",
    "Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States."
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Amgad Muneer"
    },
    {
      "affs": [
        0
      ],
      "name": "Maliazurina B Saad"
    },
    {
      "affs": [
        0
      ],
      "name": "Hui Xu"
    },
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Jia Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/9awext3qm2edr.bib",
  "cited_by": 0,
  "doi": "10.3389/fnume.2026.1956567",
  "fulltext_url": "https://europepmc.org/articles/PMC13688095",
  "id": "9awext3qm2edr",
  "issns": [
    "2673-8880"
  ],
  "landing_url": "https://europepmc.org/article/MED/42923220",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7220900277",
  "pdf_url": "https://europepmc.org/articles/PMC13688095?pdf=render",
  "pmcid": "PMC13688095",
  "pmid": "42923220",
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/9awext3qm2edr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:33Z",
      "native_id": "MED:42923220",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42923220"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "From task-specific AI to multimodal foundation models in PET/CT: a readiness framework for clinical translation.",
  "title_text": "From task-specific AI to multimodal foundation models in PET/CT: a readiness framework for clinical translation.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:33Z",
  "url": "https://research.draconitic.com/paper/9awext3qm2edr",
  "venue": "Frontiers in nuclear medicine",
  "versions": []
}