Health care executives from IBM Watson and Athenahealth athn debated that question onstage at Fortune's inaugural Brainstorm Health conference Tuesday. In addition to partnering with Celgene celg to better track negative drug side effects, IBM ibm is applying its cognitive computing AI technology to recommend cancer treatment in rural areas in the U.S., India, and China, where there is a dearth of oncologists, said Deborah DiSanzo, general manager for IBM Watson Health. For example, IBM Watson could read a patient's electronic medical record, analyze imagery of the cancer, and even look at gene sequencing of the tumor to figure out the optimal treatment plan for a particular person, she said. "That is the promise of AI--not that we are going to replace people, not that we're going to replace doctors, but that we really augment the intelligence and help," DiSanzo said. Athenahealth CEO Jonathan Bush, however, disagreed.
After conventional methods of detection failed, a team of Japanese researchers from the University of Tokyo's Institute of Medical Science used IBM Watson to successfully diagnose a 60 year-old woman where physicians were unable to, according to NDTV. The patient was initially diagnosed with acute myeloid leukemia, but treatments for that condition proved ineffective. Watson was able to identify the more rare form of leukemia she suffered from and ultimately provide a different, more successful form of treatment, according to the report. Artificial intelligence systems like IBM Watson may still be a ways off from being regularly used in hospitals, as they require large amounts of comparative data, according to Engadget. However, when given access to that type of information, AI systems can work quickly -- Watson produced the accurate diagnosis for the Japanese patient after comparing her genetic data against a database of 20 million researcher papers in just ten minutes.
IBM's Watson has done everything from winning at Jeopardy to cooking exotic meals, but it appears to have accomplished its greatest feat yet: saving a life. University of Tokyo doctors report that the artificial intelligence diagnosed a 60-year-old woman's rare form of leukemia that had been incorrectly identified months earlier. The analytical machine took just 10 minutes to compare the patient's genetic changes with a database of 20 million cancer research papers, delivering an accurate diagnosis and leading to proper treatment that had proven elusive. Watson has also identified another rare form of leukemia in another patient, the university says.
Singularity University just concluded their very first APAC Global Impact Challenge (GIC), and two Taiwanese startups have emerged as winners. The challenge aimed to discover moonshot innovations and startups that positively impact the lives of people living in the Asia Pacific, specifically with an ability to scale and impact a billion of people in a decade. Participants were tasked with developing Artificial Intelligence (AI) applications to address global issues posing a threat to sustainability. AI solutions could tackle issues ranging from energy, environment, food, water, disaster resilience, governance, and health, among other things. One of its Taiwanese winners is a startup named Vibrasee, which uses deep learning to determine the early onset of Parkinson's disease.
Recent American news events range from horrifying to dystopian, but reading the applications of our fast.ai I was blown away by how many bright, creative, resourceful folks from all over the world are applying deep learning to tackle a variety of meaningful and interesting problems. Their passions range from ending illegal logging, diagnosing malaria in rural Uganda, translating Japanese manga, reducing farmer suicides in India via better loans, making Nigerian fashion recommendations, monitoring patients with Parkinson's disease, and more. Our mission at fast.ai is to make deep learning accessible to people from varied backgrounds outside of elite institutions, who are tackling problems in meaningful but low-resource areas, far from mainstream deep learning research. Our group of selected fellows for Deep Learning Part 2 includes people from Nigeria, Ivory Coast, South Africa, Pakistan, Bangladesh, India, Singapore, Israel, Canada, Spain, Germany, France, Poland, Russia, and Turkey.