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) …
An example of how AI improves patient care is Amsterdam UMC's partnership with SAS. The project was able to clinically diagnose patients with colorectal liver cancer, the third most common cancer worldwide, using Computer vision and predictive analysis. Previously, this process required manual examination which was time-consuming and subjective to the radiologist. Automating this process has increased accuracy and saved time to ensure patient survival. Whether it's image analysis to detect cancer or other diseases immediately, predicting the number of patients to ensure the right number of doctors and hospital beds are available or using natural language processing (NLP) to understand lengthy patients reports – the potential for technological enhancement in healthcare is colossal.
Researchers at University of Washington and University of California, Los Angeles, have developed an artificial intelligence system that could help pathologists read biopsies more accurately, and lead to better detection and diagnosis of breast cancer. Doctors examine images of breast tissue biopsies to diagnose breast cancer. But the differences between cancerous and benign images can be difficult for the human eye to classify. This new algorithm helps interpret them -- and it does so nearly as accurately or better than an experienced pathologist, depending on the task. The research team published its results Aug. 9 in the journal JAMA Network Open.
Last week, Eko Devices announced a new service that matches ECG and heart sound recordings with clinical data to help pinpoint novel drug-data combinations. The Silicon Valley startup is pitching the platform, called Eko Home, as a resource for clinical trials targeting new therapies. The new platform is already seeing some action. According to the company, an ongoing Mayo Clinic study exploring how carvedilol-based cardiovascular therapies could reduce heart failure or other heart function declines among breast cancer patients undergoing chemotherapy is using the Eko Home platform to drive insights. Eko -- which is best known for its Eko Duo device, a smart remote monitor that's part stethoscope, part ECG -- also said in its announcement that it "expects to offer the drug-data combinations with other life science partners by the end of the year with additional plans to offer its SDK to hospitals and healthcare providers that wish to build the platform directly into their applications."
Few ideas in the last decade have provoked as much excitement, or as much confusion, as the introduction of artificial intelligence (AI) in oncology. From the first moment we announced our plans to apply our Watson technology to help oncologists, we were met with a stark dichotomy of emotion. The headlines ran the spectrum from hype (your next doctor might be a robot!) to cynicism (5 reasons AI in healthcare will fail). Today, five years into the journey to help improve cancer treatment through data, analytics and AI, while we're still very much in the early stages, I'm happy to report that the real-world progress is far more encouraging than either of those early storylines would suggest. In fact, not only is AI being used to support physicians in the delivery of cancer care today, it is producing quantifiable results while charting a course for the future.
It is almost 40 years since a full-body magnetic resonance imaging (MRI) machine was used for the first time to scan a patient and generate diagnostic-quality images. The scanner and signal processing methods needed to produce an image were devised by a team of medical physicists including John Mallard, Jim Hutchinson, Bill Edelstein and Tom Redpath at the University of Aberdeen, leading to the widespread use of the MRI scanner, now a ubiquitous tool in radiology departments across the world. MRI was a game-changer in medical diagnostics because it didn't require exposure to ionising radiation (such as X-rays), and could generate images on multiple cross-sections of the body with superb definition of soft tissues. This allowed, for example, the direct visualisation of the spinal cord for the first time. Most people today will have undergone an MRI or know somebody who has.
The government's announcement of a £250 million investment into artificial intelligence (AI) is very exciting and will solve some of the healthcare systems most difficult challenges. Although the UK is making leeway in the battle against cancer, the breakthroughs are only significant if early disease detection is made sooner rather than later, helping the treatments work more efficiently. Early detection of various diseases is crucial, and in cases like ovarian cancer, a woman has no symptoms in the early stages. AI and genomics can possibly help detect this cancer early, which means treatments can start sooner and more lives can be saved. AI is already making practical improvements in the healthcare system.
Breast cancer is the leading cause of cancer-related death among women, and it is difficult to diagnose. Nearly 1 in 10 cancers is misdiagnosed as not cancerous; on the other hand, the more mammograms a woman has, the greater the chance she will see a false positive result and face an unnecessary invasive procedure--most likely a biopsy. More accurate diagnostic techniques are emerging. But what if instead we relied on the guidance of an algorithm? Assad Oberai, Hughes Professor in the Aerospace and Mechanical Engineering Department at the USC Viterbi School of Engineering, asked this exact question in a recent paper published in ScienceDirect.
The National Health Service England is planning to set up a national artificial intelligence laboratory to enhance the medical care and research facility. According to the Health Secretary, Matt Hancock said AI has'enormous power' to improve the health care facilities, and save lives. The health service has announced £250m on setting up a research lab to boost AI within the health sector. However, AI will pose new challenges in protecting patient data. Many AI tools have proven to be game-changer devices, which help doctors at spotting lung cancer, skin cancer, and more than 50 eye conditions from scans.
Deep learning is a subset of machine learning, a branch of artificial intelligence that configures computers to perform tasks through experience. Deep learning, an advanced artificial intelligence technique, has become increasingly popular in the past few years, thanks to abundant data and increased computing power. It's the main technology behind many of the applications we use every day, including online language translation and automated face-tagging in social media. This technology has also proved useful in healthcare: Earlier this year, computer scientists at the Massachusetts Institute of Technology (MIT) used deep learning to create a new computer program for detecting breast cancer. Classic models had required engineers to manually define the rules and logic for detecting cancer, but for this new model, the scientists gave a deep-learning algorithm 90,000 full-resolution mammogram scans from 60,000 patients and let it find the common patterns between scans of patients who ended up with breast cancer and those who didn't.
You are free to share this article under the Attribution 4.0 International license. A new artificial intelligence system could help pathologists read biopsies more accurately, and lead to better detection and diagnosis of breast cancer, researchers say. Doctors examine images of breast tissue biopsies to diagnose breast cancer. But the differences between cancerous and benign images can be difficult for the human eye to classify. The new algorithm helps interpret them, and does so nearly as accurately or better than an experienced pathologist, depending on the task.