Many complicated diseases make up cancer.
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The American Society of Clinical Oncology’s Cancer.Net provides “individualized guides for more than 120 types of cancer and related hereditary syndromes.” Selecting the right cancer treatment for an individual patient gets even more complex when considering the various diagnostic tests, the wide range of medicines and treatments, and different treatment regimens. With so much information to consider and analyze, artificial intelligence (AI) might be, well, just what the doctor ordered.
Instead of relying on the human brain—or even teams of experts—oncologists can now turn to high-powered computation. Although AI might enhance oncology in many ways, here we will examine using these computational tools to make better decisions about an individual patient’s treatment. This includes the an oncologist’s tools such as digital pathology, molecular testing using a patient’s DNA sequence, and more, to select an appropriate medication.
When asked about the main pathological challenges in oncology that artificial intelligence might address, Manuel Salto-Tellez, chair of molecular pathology at UK-based Queen’s University Belfast, mentioned four: analysis of tissues ahead of next-generation sequencing (NGS), objective scoring of biomarkers associated with therapeutic intervention, more accurate determination of phenotypic features related to prognosis and/or therapeutic intervention, and data processing/AI as a surrogate of molecular status. He called these “four main challenges in the practice of surgical pathology that digital pathology—together with artificial intelligence—has the opportunity of solving.”
Some tools already use AI to improve oncology. Others remain in development, but promising. Nonetheless, some experts think that AI is not ready for oncology applications. Everyone interviewed here, though, believes that AI is changing oncology now or soon will.
Advances Allowing AI
Although AI is not new—emerging as a field of thought in the 1950s—scientists can now do unique things with it. Hans Hofstraat, head of global research for precision digital solutions at Netherlands-based Royal Philips first applied AI in marine environmental research about 30 years ago, but today he sees many features that expand how it can be used. “The big difference now is in ubiquitous digitization, computational power and access to huge datasets,” he said. “Digital pathology datasets, for instance, are quite humongous, and you need high-performance computing to use AI with these apps.”
But big datasets aren’t enough. “You need really good, well-annotated datasets for the analysis,” Hofstraat said. “And now, we make all of this data available and create an interface so that it can be used with AI.” This information can all be combined to create insights and information that, Hofstraat said, “can be made available to healthcare professionals and patients, so decisions can be made.”
Plus, today’s scientists can easily gain access to extremely powerful computing. Even a modern laptop outruns the computing resources available to scientists at the birth of AI. Moreover, today’s tools for crunching big data are far easier to use. […]