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@ARTICLE{Kper:275356,
      author       = {A. Küper$^*$ and P. Blanc-Durand and A. Gafita and D.
                      Kersting$^*$ and W. P. Fendler$^*$ and C. Seibold and A.
                      Moraitis$^*$ and K. Lückerath$^*$ and M. L. James and R.
                      Seifert$^*$},
      title        = {{I}s {T}here a {R}ole of {A}rtificial {I}ntelligence in
                      {P}reclinical {I}maging?},
      journal      = {Seminars in nuclear medicine},
      volume       = {53},
      number       = {5},
      issn         = {0001-2998},
      address      = {Duluth, Minn.},
      publisher    = {Saunders},
      reportid     = {DKFZ-2023-00725},
      pages        = {687-693},
      year         = {2023},
      note         = {2023 Sep;53(5):687-693},
      abstract     = {This review provides an overview of the current
                      opportunities for integrating artificial intelligence
                      methods into the field of preclinical imaging research in
                      nuclear medicine. The growing demand for imaging agents and
                      therapeutics that are adapted to specific tumor phenotypes
                      can be excellently served by the evolving multiple
                      capabilities of molecular imaging and theranostics. However,
                      the increasing demand for rapid development of novel,
                      specific radioligands with minimal side effects that excel
                      in diagnostic imaging and achieve significant therapeutic
                      effects requires a challenging preclinical pipeline: from
                      target identification through chemical, physical, and
                      biological development to the conduct of clinical trials,
                      coupled with dosimetry and various pre, interim, and
                      post-treatment staging images to create a translational
                      feedback loop for evaluating the efficacy of diagnostic or
                      therapeutic ligands. In virtually all areas of this
                      pipeline, the use of artificial intelligence and in
                      particular deep-learning systems such as neural networks
                      could not only address the above-mentioned challenges, but
                      also provide insights that would not have been possible
                      without their use. In the future, we expect that not only
                      the clinical aspects of nuclear medicine will be supported
                      by artificial intelligence, but that there will also be a
                      general shift toward artificial intelligence-assisted in
                      silico research that will address the increasingly complex
                      nature of identifying targets for cancer patients and
                      developing radioligands.},
      subtyp        = {Review Article},
      cin          = {ED01},
      ddc          = {610},
      cid          = {I:(DE-He78)ED01-20160331},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37037684},
      doi          = {10.1053/j.semnuclmed.2023.03.003},
      url          = {https://inrepo02.dkfz.de/record/275356},
}