{
  "abstract": "Medical imaging has served as primary proving ground for clinical AI, yet a decade of intense research has not translated into proportionate bedside impact. We argue that this gap is not primarily a product of insufficient algorithmic performance, inadequate regulation, or limited explainability. Rather, it reflects a structural misalignment, between how AI systems are designed and evaluated, and how clinical decisions are made. This work identifies 8 interconnected dimensions of this misalignment: 1) the dominance of pixel-only models in a multimodal clinical world; 2) the erosion of physician trust through opaque and inflexible systems; 3) the unfulfilled promise of foundation models in data-sparse medical domains; 4) the persistent bottleneck of non-shareable under-curated datasets; 5) the gap between validated algorithms and deployable clinical platforms; 6) the slow, unmonitored translation of evidence into routine practice; 7) the risk of AI displacing rather than extending physician judgment; and 8) the failure of prediction-centric AI to generate actionable, agentic clinical guidance. For each dimension, we reframe the problem and propose a path forward, culminating in a vision of physician-centered AI solutions with agentic reasoning that extend, rather than replace, clinical judgment.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.27428",
  "author_total": 5,
  "authors": [
    {
      "name": "Arman Rahmim"
    },
    {
      "name": "Nourhan Bayasi"
    },
    {
      "name": "Xiaoxiao Li"
    },
    {
      "name": "Babak Saboury"
    },
    {
      "name": "Fereshteh Yousefirizi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5getw6nwzmbzn.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.27428",
  "fulltext_url": null,
  "id": "5getw6nwzmbzn",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.27428",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.AI"
    ],
    [
      "arxiv",
      "physics.med-ph"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7171967461",
  "pdf_url": "https://arxiv.org/pdf/2607.27428",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-02",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/5getw6nwzmbzn.ris",
  "sources": [
    {
      "first_seen": "2026-11-03T12:21:04Z",
      "native_id": "2607.27428",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.27428"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.med-ph",
      "subfield": "biological-medical"
    }
  ],
  "title": "Rethinking AI in Medical Imaging: Assumptions, Reality, and Reframing; Towards Physician-Centered Agentic Reasoning",
  "title_text": "Rethinking AI in Medical Imaging: Assumptions, Reality, and Reframing; Towards Physician-Centered Agentic Reasoning",
  "type": "preprint",
  "updated_at": "2026-11-05T15:20:21Z",
  "url": "https://research.draconitic.com/paper/5getw6nwzmbzn",
  "venue": "arXiv",
  "versions": []
}