In this industry and in everyday life, the best work and interactions comes from people solving problems for other people. IBM's Watson was created to answer questions on the TV gameshow Jeopardy! But now IBM are using Watsons question answering abilities to help nurses in a lung cancer treatment ward in the Memorial Sloan Kettering Cancer Center, New York, to get diagnoses quicker. Instead of giving bots and AI a soul, give the being with a soul more support with bots and AI.
Notably, Novartis (NYSE:NVS), which has also been involved in AI for two or three years, recently signed a deal with IBM Watson to explore the technology's use in breast cancer care. The collaboration's aims include identifying better treatment sequences or predictors of response, Pascal Touchon, Novartis' global head of oncology strategy, told EP Vantage. Also looking for patterns is London-based BenevolentAI, which hopes its machine-based learning approach to processing academic research, clinical studies and other health-related data will help identify correlations in data that could lead to new drugs and significantly speed up the process of drug development. With plenty of other companies clamoring to get into healthcare, including tech giants like IBM Watson and Alphabet, how will medtech and pharma groups compete in the AI space?
As the CEO of Silicon Valley Artificial Intelligence, Pete Kane has founded multiple startups such as Healthcare Minnesota and Startup Venture Loft, which led to his most recent collaborative creation Silicon Valley Artificial Intelligence. The community group uses machine learning (ML) and artificial intelligence (AI) to collaborate on research projects that can make landmark discoveries in science and healthcare. Silicon Valley AI will host the Genomics Hackathon from Friday through Sunday at Google Launchpad in San Francisco. In the future, I believe AI will play a leading role in areas like drug discovery, personalized medicine and cancer genomics.
Like Khosla, many have argued automation and smarter AI applications could have a drastic effect on workers and make their jobs obsolete. Khosla argued governments and industry analysts will need to contend with this possible risk in the future. While AI has commonly been seen as a science fiction pipe dream, the field has still seen a drastic upswing in investment among tech companies in the past few years. Facebook touted its use of AI in its counterterrorism prevention efforts while EA hopes to integrate AI and machine learning to power its future video game releases.
This enables accurate accurate and real-time decision making, improving overall efficiency and reducing costs. Following criticism in 2016, DeepMind is building a blockchain distributed ledger system to monitor patient data and allow healthcare professionals to ensure records are kept securely. By applying machine learning in healthcare to the challenge of patient diagnosis, the digital health start-up believes it can efficiently identify health risks. 'In healthcare, machine learning could help provide more accurate diagnoses and more effective healthcare services, through advanced analysis that improves decision-making', according to a report on AI by The Royal Society.
AI is the ability of a computer to process a vast amount of information for you, make decisions, and take (and/or advise you to take) appropriate action. Watson made headlines back in 2011 by winning Jeopardy, and now it's helping doctors treat cancer patients by processing massive amounts of clinical data and cross-referencing thousands of individual cases and medical outcomes. But these are the early, "weak" versions of AI. Today, Google's search engine gives a teenager with a smartphone in Mumbai and a billionaire in Manhattan equal access to the world's information.
Citing another study focused on breast cancer lymph node biopsies, Beck showed the difference between the hotspots found by a computer versus a human pathologist; the former highlighted many additional areas that turned out to contain cancer cells. "We provide pathologists with both the raw image, so they're still looking at the data they're used to, as well as the image processed by the learning system, which essentially identifies the areas of cancer, enabling a physician to focus in on those areas," said Beck. The breast cancer study found that without AI, this kind of biopsy only has an accuracy rate of 85 percent. "What's emerged is this incredible commoditization of so many parts of computer vision and machine learning that used to require teams of PhDs to develop in terms of infrastructure," said Cornell Tech Professor and Summit coorganizer Serge Belongie, "but now it's possible for individual hackers or developers on small startup teams to bring that kinds of functionality to any kind of product."
Oncology teams currently review cancer patient cases and studies themselves, working diligently to develop treatment strategies. After reading twenty-five million medical studies and trials (its first week on the job, mind you) and scanning the internet for additional trial information, Watson was put to the test, analyzing one thousand actual oncology cases alongside oncology review teams. This announcement opens new horizons for their employees, as well as another opportunity to develop Watson through actual case studies. "The system can be utilized to drive key insights from massive amounts of information, and it can learn how to present it in a useful way as it learns."
At Memorial Sloan Kettering Cancer Center, physicians are training a new kind of colleague: the Watson for Oncology program. "Just like we would teach a trainee in medical oncology or surgical oncology, we're teaching the MSK Watson system," explained Dr. Mark Kris, a lead physician at Memorial Sloan Kettering. Doctors feed information into Watson for Oncology, allowing it to learn appropriate treatment options. These recommendations are doctor-vetted since, as with Watson for Oncology, the system is'taught' by actual physicians who feed data and information in.