What can artificial intelligence teach us about the molecular mechanisms underlying disease?

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While molecular imaging with positron emission tomography or single-photon emission computed tomography already reports
on tumour molecular mechanisms on a macroscopic scale, there is increasing evidence that there are multiple additional features
within medical images that can further improve tumour characterization, treatment prediction and prognostication. Early reports
have already revealed the power of radiomics to personalize and improve patient management and outcomes. What remains
unclear is how these additional metrics relate to underlying molecular mechanisms of disease. Furthermore, the ability to deal
with increasingly large amounts of data from medical images and beyond in a rapid, reproducible and transparent manner is
essential for future clinical practice. Here, artificial intelligence (AI) may have an impact. AI encompasses a broad range of
‘intelligent’ functions performed by computers, including language processing, knowledge representation, problem solving and
planning. While rule-based algorithms, e.g. computer-aided diagnosis, have been in use for medical imaging since the 1990s, the
resurgent interest in AI is related to improvements in computing power and advances in machine learning (ML). In this review we
consider why molecular and cellular processes are of interest and which processes have already been exposed to AI and ML
methods as reported in the literature. Non-small-cell lung cancer is used as an exemplar and the focus of this review as the most
common tumour type in which AI and ML approaches have been tested and to illustrate some of the concepts.
Original languageEnglish
Pages (from-to)2715-2721
Number of pages7
JournalEuropean journal of nuclear medicine and molecular imaging
Issue number13
Early online date12 Jun 2019
Publication statusPublished - 1 Dec 2019


  • Artificial intelligence
  • Machine learning, deep learning
  • Molecular imaging
  • Radiomics


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