Few technological advancements have been as divisive as artificial intelligence (AI), especially in matters where human lives could hang in the balance. These AI solutions make complex treatment recommendations for cancer patients based on their individual characteristics and risk factors. Roughly 60 percent of the WHO's "essential" medicines are unavailable in these countries, due partially to the cost and partially due to a lack of medical professionals in the area. Developing AI in the medical industry, further than it's already gone, is going to help us resolve the forthcoming doctor shortage, mitigate the ever-rising costs of healthcare, and bring adequate medical treatment to areas of the world that are currently without it.
Zipline has hired locals to operate its drones and run the distribution centers, which stock blood products and medical supplies. Yet "two-way services are what's actually needed for clinical care," says Amukele, who heads clinical pathology labs at Johns Hopkins' Bayview Medical Center. Delivering emergency supplies and picking up diagnostic test samples require aircraft with different capabilities, says Jeff Street, a drone engineer and pilot. Street and Amukele have shown that unmanned aircraft can safely ship a range of clinical specimens and recently set a new distance record for medical drone transport -- a three-hour flight carrying human blood samples across 161 miles of Arizona desert.
Desperate, the doctors called a distribution center near Kigali, where clinic workers and a flight crew loaded a series of small, unmanned aircraft with the needed supplies and launched them into the sky. The Tanzanian government wants to make as many as 2,000 daily deliveries from four distribution centers serving an area roughly the size of Texas and Louisiana. Each can carry 3 pounds of cargo (one unit of blood weighs roughly 1.2 pounds), and the batteries can make a round trip of 100 miles. Zipline makes a habit of recruiting and training local engineers, health workers, and flight operators.
"The system could potentially be an aid for doctors in the ICU, which is a high-stress, high-demand environment," says PhD student Harini Suresh, lead author on the paper about ICU Intervene. "The goal is to leverage data from medical records to improve health care and predict actionable interventions." Another team developed an approach called "EHR Model Transfer" that can facilitate the application of predictive models on an electronic health record (EHR) system, despite being trained on data from a different EHR system. "Much of the previous work in clinical decision-making has focused on outcomes such as mortality (likelihood of death), while this work predicts actionable treatments," Suresh says.
The debate between artificial intelligence (AI) and intelligence augmentation (IA) has been raging for decades. Will computer systems replace humans with a faster, smarter form of intelligence (AI) or will they augment our existing human intelligence and work alongside us (IA)? The current trajectory of AI suggests that while some jobs will undoubtedly be taken by machines, the advances we're making in AI will, by and large, augment us, not replace us. By handing things like scheduling meetings, booking travel, finding trends in large data sets, and reviewing legal documents over to autonomous agents, we're freeing up massive amounts of time for humans to focus on the things we're best at.
What the research collaboration will attempt to do is create an entry point in the field of precision medicine -- combining JDRF's connections to research teams around the globe, and its subject matter expertise in T1D research, with the technical capability and computing power of IBM. IBM scientists will look across at least three different data sets and apply machine learning algorithms to help find patterns and factors that may be at play, with the goal of identifying ways that could delay or prevent T1D in children. As a result, JDRF will be in a better position to identify the top predictive risk factors for T1D, cluster patients based on top risk factors, and explore a number of data-driven models for predicting onset. The deep expertise our team has in artificial intelligence applied to healthcare data makes us uniquely positioned to help JDRF unlock the insights hidden in this massive data set and advance the field of precision medicine towards the prevention and management of diabetes."
While the 21st Century Cures Act that passed last December exempted certain CDS from regulation and indeed FDA intends to exempt even more, FDA will continue to regulate high risk CDS. Initially such software was placed in class III – the highest regulatory oversight for products with the greatest risk – but more recently FDA has regulated that software in class II for products of only moderate risk. In the 2012 guidance documents, FDA lists information such as algorithm design, features, models, classifiers, the data sets used to train and test the algorithm, and the test data hygiene used. FDA has also begun to receive submissions to clear software that employs machine learning in what the agency refers to as "adaptive systems" – systems that evolve over time based on the new evidence collected in the field after the device goes to market.
Cardiac pain originates from the heart muscle, most typically when blood flow to the heart (through vessels called coronary arteries) become blocked. In the heart muscle, there are nerve endings which transmit signals to the brain which get interpreted as chest pain. Unfortunately, just like other pain arising in other organs in the body, cardiac pain is poorly localized. Topping it off, different healthcare professionals may also interpret your description of pain very differently.
"But roughly 5 to 15 percent of the general population will have some experience of hearing unusual voices at some point in their lives. "There's an increasingly popular theory on how our brain makes sense of the world. To see if priming might play a role in hearing voices, Alderson-Day and his colleagues including researchers from University College London, and the University of Porto in Portugal, took two groups of people--those who claimed to hear voices but were otherwise mentally healthy and those who were also healthy but didn't hear voices--and placed them into functional magnetic resonance imaging (fMRI) machines. Because a lot of us have some experience hearing voices--if you've ever heard a voice (your mom perhaps) calling your name in an empty house you've experienced some level of auditory hallucination--only people who had recently and relatively frequently heard voices were included in this group.