If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
In a related editorial, R. Jeffrey Westcott, MD, and James E. Tcheng, MD, said Zack and colleagues' findings support the idea that machine learning could outperform classical statistical approaches to risk prediction--but it'll take some work to make it an industry standard. "Transforming healthcare, and, more specifically, transforming the management of data within healthcare to enable AI and its siblings, requires foundational investment and culture change," the editorialists wrote. They said artificial intelligence and machine learning will undoubtedly become "increasingly important in clinical medicine" as we move forward, with equity funding for healthcare-related AI ventures topping $2.4 billion in 2018. "Machine learning has proven to be valuable and is therefore the future," Westcott and Tcheng wrote. "Data warehouses and data lakes contain amazing amounts of structured and unstructured data that will change how medical research, drug and device trials, and device tracking are done. A collaborative effort is needed with EHR vendors, third-party vendors, professional societies and others to start meaningful standardized data collection and workflow redesign now."
What if technology could predict a hereditary disease you could stop from progressing? What if a visit to your primary physician for carpal tunnel syndrome ended with a suggestion to get tested for a rare illness? As Komodo Health's artificial intelligence algorithms crunch a decade of data about health conditions across several hundred million Americans, many what-if scenarios are becoming pathways for the next clinical assessment to be taken. Founded in 2014, Komodo, funded by Felicis Ventures and McKesson Ventures, has mapped out 300 million individual health identities across the country to find patterns signaling the presence of disease, years before they're ever diagnosed. At a time when chronic conditions account for 75 percent of the $3 trillion US healthcare spend annually, identifying when symptoms occur earliest or recognizing patterns of activity that are often a precursor to the manifestation of diseases is vital in preventing those economically, physically and mentally crippling illnesses to either exist or progress.
Personalized Analytics is becoming essential in healthcare, stemming from the movement from fee-for-service to a value-based market. The need to preempt and prevent disease on a more personal level, rather than merely reacting to symptoms, has created a significant opportunity for machine learning-based applications. This "analytics of one" approach (using advanced mathematical models and artificial intelligence techniques) is already impacting several key areas: Prime examples include cardiac imaging analysis that aides physicians in assessing conditions, including heart attacks and coronary artery disease, and retinal image analysis to detect diabetic retinopathy. The anticipated goal for AI in healthcare is to enhance and expand the "four Ps" of care delivery – predictive, preventative, personalized and participatory. Predictive: Predictions have existed in healthcare for some decades now, as statistical models based on structured data sources.
A new method for evaluating electrocardiographic signals has been shown to be more effective than cardiac telemetry in detecting atrial fibrillation in stroke patients. The approach--called electrocardiomatrix--converts two-dimensional signals from the ECG into a three-dimensional matrix that enables fast, accurate and intuitive detection of cardiac arrhythmias, according to researchers at Michigan Medicine. "We validated the use of our technology in a clinical setting, finding the electrocardiomatrix was an accurate method to determine whether a stroke survivor had an Afib," says co-inventor Jimo Borjigin, an associate professor of neurology and molecular and integrative physiology at Michigan Medicine. Borjigin and her colleagues recently published results in the journal Stroke from a single-center, prospective observational study. "Electrocardiomatrix results were compared with the clinical team's medical record documentation of AF identified through telemetry," state the authors.
Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential outcomes approaches, often include processing steps before the final estimation step. The purposes of this paper are: (i) to overview the recent emergence of prediction underpinning steps in contemporary causal inference methods as a useful perspective on contemporary causal inference methods, and (ii) explore the role of machine learning (as one approach to'best prediction') in causal inference. Causal inference methods covered include propensity scores, inverse probability of treatment weights (IPTWs), G computation and targeted maximum likelihood estimation (TMLE). Machine learning has been used more for propensity scores and TMLE, and there is potential for increased use in G computation and estimation of IPTWs.
As we all know, finding out you have any form of terminal illness is a scary prospect. It may sometimes seem difficult to be positive minded & find a way to get through it. Nowadays, technology is evolving rapidly, and the help and development of new technology in treatment is helping people not only live longer but determine the most effective way to treat their illness. There are some crazy but amazing technologies out there that help medical professionals monitor and prevent ill health. MC10 develops a Bio stamp that is'thinner than a plaster', which can monitor temperature, sense any movement and heart rate – all these things are especially important when it comes to our health, all this data can then be transmitted wirelessly.
There is an unprecedented growth in the percentage of aging population throughout the world, particularly in growing economies such as Europe, Japan and China. Form 2000 to 2050, the percentage of the world's population who is 60 years of age and older will approximately double from about 12% to 22% (from 605 million to 2 billion). During the same period, the number of people aged 80 years and older will quadruple. In the USA, 14.5% of the population is 65 years or older, but by 2030 these number is anticipated to grow to 20%. This rapid aging demographic will directly affect social, economic and health outcomes for these growing economies.
Eric Topol is an American cardiologist and geneticist – among his many roles he is founder and director of the Scripps Research Translational Institute in California. He has previously published two books on the potential for big data and tech to transform medicine, with his third, Deep Medicine, looking at the role that artificial intelligence might play. He has served on the advisory boards of many healthcare companies, and last year published a report into how the NHS needs to change if it is to embrace digital advances. Your field is cardiology – what makes you tick as a doctor? I was in clinic all day yesterday – I love seeing patients – but I also try to use whatever resources I can, to think about how can we do things better, how can we have much better bonding, accuracy and precision in our care.
AI is transforming the health industry. As a society we are becoming more data hungry than ever before, and this is also evident at an individual level though our growing fascination with wearable technology and e-health. Previously, we've summed up AI and deep learning from a beginner's perspective, and discussed some of their use cases in healthcare, specifically medical diagnoses. Below, we continue in healthcare, providing a brief overview of deep learning in drug discovery, e-health and electronic health records. Drug discovery from idea conception to a marketable product, can take over decade, and on average costs US$2.6 billion.
In 2009, the GuideLiner Catheter revolutionized the concept of guide extension, creating new possibilities in interventional cardiology. Now in its third generation, the GuideLiner V3 Catheter continues to build on a history of innovation and performance -- one that has been demonstrated with more than half a million catheters in cath labs around the world. Teleflex also offers a family of Turnpike Catheters. These contain a robust multi-layer shaft that provides impressive flexibility, torque and tracking over a 0.014" guidewire in complex coronary and peripheral interventions. The unique five-layer composite shaft provides an ideal combination of flexibility and torque response to help navigate through complex anatomy while the outer polymer layer paired with a 60 cm distal hydrophilic coating facilitates smooth catheter delivery.