Machine learning is a form of narrow AI used to classify data and make predictions. Supervised machine learning classifies orthopaedic images comparably to humans. Neural networks identify successful exercise performance with 99.4% accuracy. Machine learning can predict successful performance of a single leg squat exercise. Unsupervised learning finds patterns in data without training; used in data mining.
Researchers at Washington University in St. Louis are developing a new imaging technique that can reportedly provide accurate, real-time, computer-aided diagnosis of colorectal cancer. Using deep learning, a type of machine learning, the team used the technique on more than 26,000 individual frames of imaging data from colorectal tissue samples to determine the method's accuracy. Compared with pathology reports, they were able to identify tumors with 100% accuracy in this pilot study. This is the first report ("Real-time colorectal cancer diagnosis using PR-OCT with deep learning") using this type of imaging combined with machine learning to distinguish healthy colorectal tissue from precancerous polyps and cancerous tissue. Results appear in advance online publication in the journal Theranostics.
Life sciences companies are likely to begin experimenting further with AI in their workflows in the coming years, but they face challenges in AI adoption due to strict regulations. Machine learning has a "black box" problem, meaning that it's in many cases impossible to know how a machine learning algorithm comes to its conclusions. An AI application that detects cancer, for example, may not be able to show an oncologist how it determined the presence of cancer in a patient's body. As a result, if the oncologist used the application to diagnose a patient, they wouldn't be able to explain to the patient what makes them sure they have cancer. This issue relegates AI applications in life sciences to experiments and pilots, and widespread adoption, although likely inevitable, may not come for a while as public opinion shifts toward accepting that its diagnoses are informed by decision-making artificial intelligence and regulations evolve to match.
Technology is ever-changing, and with every new decade it accelerates at a pace that is often difficult for us humans to keep up with. We now live in a world in which we rely heavily on technology on a daily basis, regardless of which field we belong to. However, few industries have been changed by technology as much, or as positively, as healthcare. Although the healthcare industry has often lagged behind others when it comes to deploying advanced technology, new discoveries and improvements are always being made. The technology that we know today has opened countless doors and opportunities to improve our lives, and it's almost impossible to imagine life without it now.
Today's fast-paced life has many challenges, which has led to the Millenials being called as the Burnout Generation. A report by the World Health Organisation (WHO) predicts that by 2020, 20% of the Indian population will suffer from mental illnesses. The report also says that by next year, depression will be the second-largest disease burden for the entire world. But now, artificial intelligence is making its presence felt in this sector. For example, researchers at IBM are using transcripts and audio inputs from psychiatric interviews, coupled with machine learning techniques to find patterns in speech.
How on earth could a non-living digital device be of value to any human being experiencing mental health issues? Can a person really develop a sense of trust with AI like they might have with another person? Or, even more subtlety, what if over time the artificial intelligence, or AI, is developing algorithms and drawing conclusions about people's mental health that are incorrect, biased or even discriminatory? Currently, countries all around the world are grappling with the high prevalence of mental health disorders and their enormous impact on people's day-to-day lives, the community, and society as a whole. The truth is that mental health workforces, even when well-funded and supported in developed nations, aren't able to keep up with the demand.
Deep learning, a type of artificial intelligence, can boost the power of MRI in predicting attention deficit hyperactivity disorder (ADHD), according to a study published in Radiology: Artificial Intelligence. Researchers said the approach could also have applications for other neurological conditions. The human brain is a complex set of networks. Advances in functional MRI, a type of imaging that measures brain activity by detecting changes in blood flow, have helped with the mapping of connections within and between brain networks. This comprehensive brain map is referred to as the connectome.
Developing prescription drugs is a high-cost, high-risk endeavor. Average research and development for an approved prescription drug requires an investment of $2.9 billion and takes more than 11 years. Clinical trials alone can cost an average of $1.1 billion over 6.6 years. In fact, clinical trials account for a staggering 40 percent of the pharmaceutical industry's research budget. To make matters worse, only 14 percent of drugs that enter clinical trials are eventually approved.
It's that time when we start to look ahead to what next year holds for the life science sector...Lu Rahman outlines 2020s big medtech players A decade ago the healthcare advances create by AI would have seemed the stuff of dreams. But back in 2018 Theresa May announced plans to use artificial intelligence and data to transform the way certain diseases like cancer. The technology is moving at a pace – this year we heard that a team led by the University of Surrey had filed the first ever patent for inventions autonomously created by AI without a human inventor. Professor Ryan Abbott explained the implications this had for the life science sector: "These filings are important to any area of research and development as well as any area that relies on patents. Patents are more important in the life sciences than in many other areas, particularly for drug discovery. These tasks can be the foundation for patent filings. "As AI is becoming increasingly sophisticated, it is likely to play an increasing role in R&D including in the life sciences.