Q: Computers and artificial intelligence show both great progress and great promise in many fields. Can “Big Data”, A.I., and the Watson ilk devise a way to end cancer?
A: In 2011, a young and emotionless Watson grabbed the tech industry’s attention by dominating Jeopardy’s two greatest champions. That night, the show was not at all about the $1 million prize. Instead all eyes were on the one-of-a kind contestant: Watson, IBM’s supercomputer. Watson had just proved that intelligent machines can understand, process, and respond to questions posed in human language.
A few years later, artificial intelligence (AI) exploded into many information-intensive industries with its market expected to reach $47 billion by 2020. But young Watson had one main career in mind since its inception: healthcare. It entered medical school in 2011 and graduated in 2 years.
So how can AI change the oncology field? According to a paper published by Michael Schatz in the journal PLoS Biology, genomic data is likely to generate the most raw data in the next ten years–data which holds answers to many health questions. While understanding and finding patterns in this enormous amount of data might seem impossible for the human brain, cognitive computing capable of ingesting data that analyzes and self-learns without forgetting might hold the key to uncovering the answers.
AI can therefore revolutionize oncology care, save doctors time, and save patients’ lives. Because supercomputers store millions of oncological records which they can rapidly analyze and cross-reference, their deep learning ability has the potential to:
- understand the genetic profile of a patient and offer evidence-based, personalized treatment options
- provide accurate interpretation of medical images, faster diagnosis, and appropriate treatment options
- treat rare cancers
- advance resistance research
- improve and accelerate new drug discovery
- optimize patient identification and clinical trial matching
- assist in health management and remote patient monitoring
- democratize access to healthcare, and expand knowledge from one specialist to any doctor
It is important to emphasize that the success of AI and deep learning relies heavily on the existence of a large, accurate training dataset, which in turn, relies on human input and effort. Therefore strong collaboration and data sharing between supercomputer developers and healthcare systems are essential. Not surprisingly, for instance, IBM Watson Health is partnering with more than a dozen leading cancer institutes to develop Watson for Genomics.
As the excitement of using AI in healthcare grows, the concerns about AI will grow as well. These concerns display a general lack of trust in machines and include patient record privacy and confidentiality, effective analysis of precarious studies, and the potential for human replacement. The latter fear is augmented by warning from experts such as Stephen Hawking who caution that AI could evolve faster than the human race, and that “…success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last.”
Although this point is beyond the scope of this article, it is worth mentioning that caring for a patient involves more than just finding a diagnosis and administering a cure. It is about evolving continuous relationships between the patient, the doctor and the health care team. Even if Watson and similar robots are starting to learn to act more human by analyzing emotions, tone, and personality, machines won’t be able to replace meaningful relationships between patients and practitioners because AI still lack real human feelings and morals.
Back to the main question: Can AI devise a way to end cancer?
AI is still in its infancy. Its biggest promise is to amplify human ability and augment their intelligence. It has the potential to become the doctor’s closest advisor, providing better and faster diagnostic and treatment tools to improve patient care and quality of life. But will it find an end to cancer? Only the future can tell. The hope is that it will speed up discovery and put us closer to the finish line.
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