{
  "id": "2108.02814",
  "version": "v1",
  "published": "2021-08-05T18:58:56.000Z",
  "updated": "2021-08-05T18:58:56.000Z",
  "title": "Potential Applications of Artificial Intelligence and Machine Learning in Radiochemistry and Radiochemical Engineering",
  "authors": [
    "E. William Webb",
    "Peter J. H. Scott"
  ],
  "comment": "10 pages, 4 figures",
  "categories": [
    "cs.LG"
  ],
  "abstract": "Artificial intelligence and machine learning are poised to disrupt PET imaging from bench to clinic. In this perspective we offer insights into how the technology could be applied to improve the design and synthesis of new radiopharmaceuticals for PET imaging, including identification of an optimal labeling approach as well as strategies for radiolabeling reaction optimization.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2021-08-05T18:58:56.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "artificial intelligence",
      "machine learning",
      "potential applications",
      "radiochemical engineering",
      "radiochemistry"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 10,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}