AI in Cancer Research

Online Links

  1. Nature Research: Big Data takes on Cancer
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    • Extracts new meaning from large clinical and molecular datasets using machine learning and data science technologies
    • Explains that people that did not studied cancer can make a difference as Artificial Intelligence (AI) researcher
    • Suggests that algorithmic AI models could extract more insights from the clinical records
    • Discusses how machine learning could detect tumours at an earlier stage and offer personalized treatment recommendations
    • Parse patient medical reports and develops new deep learning methods to interpret diagnostic images
    • Implements one tool for clinical practice as a diagnostic aid for radiologists
    • Reduces uncertainty and truly personalizes patient care using machine learning
    • Suggests that machine learning can help doctors to spot signs of cancer a year or two earlier, possibly before the disease had spread to lymph nodes
    • Describes the challenge that in medicine is so much raw clinical data generated (e.g. pathology lab, imaging suite, surgical ward, oncologist office) that it is rarely obvious how best to design and train AI algorithms to connect all the disparate threads of information for cancer patients
    • Informs about an event that invited pioneering thinkers in the fields of cancer medicine, tumour genetics and data science with the aim of forging connections between AI and oncology
    • Mentions Project Genomics Evidence Neoplasia Information Exchange (GENIE) that was launched in 2015 as a vehicle for sharing tumour genetic profiles from patients in active clinical Treatment.

Morris Riedel AI in Cancer Research