{
  "abstract": "Objectives: To summarize significant research contributions on cancer informatics published in 2024, an extensive search using PubMed/MEDLINE was conducted to identify the scientific contributions published in 2024 that address topics in cancer informatics. The selection process was composed of three steps: 1) 15 candidate best papers were first selected by the two section editors; 2) external reviewers from internationally renowned research teams reviewed each candidate best paper; and 3) the final selection of two best papers was conducted by the editorial board of the IMIA Yearbook.\n\nResults: The two selected best papers demonstrate advances in the clinical implementation of cancer informatics methodologies. Both studies highlight the potential for multimodal data and foundation models as well as prospective implementation of AI to influence cancer care.\n\nConclusions: Cancer informatics has evolved into a sophisticated discipline within bioinformatics, with increasing emphasis on multimodal artificial intelligence and foundation models that integrate diverse data types for comprehensive cancer analysis. As these innovative approaches mature, the cancer informatics field prioritizes robust prospective validation and deployment strategies to ensure these technologies deliver meaningful improvements in patient care and clinical outcomes.",
  "abstract_shown": true,
  "affiliations": [
    "Yale Cancer Center New Haven CT USA",
    "Department of Therapeutic Radiology, Yale School of Medicine New Haven CT USA",
    "Department of Bioinformatics and Data Science, Yale School of Medicine New Haven CT USA",
    "Emory University Atlanta GA USA",
    "Winship Cancer Institute Atlanta GA USA"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0,
        1,
        2
      ],
      "name": "Sanjay Aneja"
    },
    {
      "affs": [
        3,
        4
      ],
      "name": "Ravi B. Parikh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/3yc3vhg0sxq8z.bib",
  "cited_by": 0,
  "doi": "10.1055/s-0046-1816608",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13730713/",
  "id": "3yc3vhg0sxq8z",
  "issns": [
    "0943-4747",
    "2364-0502"
  ],
  "landing_url": "https://doi.org/10.1055/s-0046-1816608",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Section 3: Cancer Informatics - CI"
    ],
    [
      "heading",
      "Synopsis"
    ],
    [
      "openalex",
      "2718|Artificial Intelligence in Healthcare and Education"
    ]
  ],
  "openalex": "W7225203173",
  "pdf_url": null,
  "pmcid": "PMC13730713",
  "pmid": "42976794",
  "published": "2026-11-03",
  "publisher": "Thieme Medical Publishers",
  "ris": "https://research.draconitic.com/paper/3yc3vhg0sxq8z.ris",
  "sources": [
    {
      "first_seen": "2026-11-07T12:23:13Z",
      "native_id": "PMC13730713",
      "source": "pmc",
      "url": "https://doi.org/10.1055/s-0046-1816608"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2718|Artificial Intelligence in Healthcare and Education",
      "subfield": "epidemiology-public-health"
    }
  ],
  "title": "Cancer Informatics 2025: Bringing foundation models and multimodal cancer data to the forefront",
  "title_text": "Cancer Informatics 2025: Bringing foundation models and multimodal cancer data to the forefront",
  "type": "peer-reviewed",
  "updated_at": "2026-11-07T12:23:13Z",
  "url": "https://research.draconitic.com/paper/3yc3vhg0sxq8z",
  "venue": "Yearbook of Medical Informatics",
  "versions": []
}