When entrepreneurs give business advice, they consistently emphasize the importance of using available technology and digital strategies to save time and maximize results. If your business handles massive amounts of data, streamlining the way you manage and store information is essential. The healthcare industry, however, has historically failed to adopt the necessary tools and strategies for handling sensitive patient information. Adapted from technology used in the finance industry, the blockchain could be the new paradigm the medical community desperately needs. A patient's medical information may include notes from doctor visits, lists of their prescription medications and allergies, results of diagnostic tests, vaccination and surgical records, psychiatric evaluations, diagnostic codes for insurance purposes, and documentation required to receive disability accommodations or social security benefits.
Researchers have developed a new technique based on artificial intelligence and machine learning, which enable clinicians to acquire higher quality images without having to collect additional data. A radiologist's ability to make accurate diagnoses from high-quality diagnostic imaging studies directly impacts patient outcome. However, acquiring sufficient data to generate the best quality imaging comes at a cost - increased radiation dose for computed tomography (CT) and positron emission tomography (PET) or uncomfortably long scan times for magnetic resonance imaging (MRI). Now researchers with the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH) have addressed this challenge with a new technique based on artificial intelligence and machine learning. They describe the technique - dubbed AUTOMAP (automated transform by manifold approximation) - in a paper published today in the journal Nature.
We already use AI in medicine to examine medical scans and spot signs of diabetes, among other applications. In China, though, artificial intelligence can do more than just assist medical professionals: it can help alleviate the country's doctor shortage. A hospital in Beijing, for instance, will start running all its lung scans through an algorithm that can expedite the screening process starting next month. The software was developed by a Beijing-based startup called PereDoc, and it can quickly spot nodules and other early signs of lung diseases. According to MIT's Technology Review, China has been beefing up its health care facilities with AI tools as part of its nationwide AI push, especially since there are only 1.5 doctors for every 1,000 people in the country, compared with 2.5 for every thousand in the US.
A radiologist's ability to make accurate diagnoses from high-quality diagnostic imaging studies directly impacts patient outcome. However, acquiring sufficient data to generate the best quality imaging comes at a cost - increased radiation dose for computed tomography (CT) and positron emission tomography (PET) or uncomfortably long scan times for magnetic resonance imaging (MRI). Now researchers with the Athinoula A. Martinos Center for Biomedical Imaging at Massachusetts General Hospital (MGH) have addressed this challenge with a new technique based on artificial intelligence and machine learning, enabling clinicians to acquire higher quality images without having to collect additional data. They describe the technique - dubbed AUTOMAP (automated transform by manifold approximation) - in a paper published today in the journal Nature. "An essential part of the clinical imaging pipeline is image reconstruction, which transforms the raw data coming off the scanner into images for radiologists to evaluate," says Bo Zhu, PhD, a research fellow in the MGH Martinos Center and first author of the Nature paper.
On a recent day at a hospital in western Beijing, a cancer radiologist named Chongchong Wu loaded a suspicious-looking lung scan into a computer program resembling Photoshop. A neural network trained on thousands of example scans highlighted nodules in red squares, which she examined carefully. She corrected two false positives where the network mistakenly identified blood vessels as potential malignancies. But she also found a nodule that she'd previously overlooked, perhaps indicating an early sign of disease. China is embarking on a big initiative to add AI to health care with tools like this one.
The media is replete with articles about how artificial intelligence (AI) is going to change the medical world, in cancer detection and other diagnostic and treatment disciplines. The articles describe how AI, primarily deep learning (DL) applications are as accurate or better than medical experts. That means they'll be used quickly adopted, right? Not really, there's a regulatory picture many ignore. One of the first expert systems, a subset of AI, was MYCIN, initially developed as a doctoral dissertation by Edward Shortliffe, at Stanford University.
We have a great opportunity to get smarter about the way we are using AI and machine learning with datasets to improve the quality of clinical care" - Simon Stevens, chief executive of NHS England The adoption of artificial intelligence in healthcare is on the rise and is solving a variety of problems for patients, hospitals and the healthcare industry overall. Sir John Bell's Life Sciences Industrial strategy identified the faster application of Artificial Intelligence (AI) as a priority and the NHS England is to invest more in AI over the next 12 months and create new Digital Innovation Hubs. These new Digital Innovation Hubs will enable researchers to engage with a meaningful dataset. AI is increasingly being applied in healthcare and medicine, with the greatest impact being achieved thus far in medical imaging. A recent Lancet editorial entitled'Augmenting diagnostic vision with AI' suggested artificial intelligence had the potential to interpret clinical data more accurately and more rapidly than medical specialists', such as radiologist and dermatologist who analyse hundreds of thousands of images over their career.
While the healthcare industry becomes increasingly adept at applying clinical and claims data to improve care, it has largely ignored other data sources that provide the greatest opportunity to positively impact health and cost at scale. The dependence on this limited data set originates in the system's orientation toward "sick care" -- treating illness. To radically improve health care, we need to apply consumer demographic and lifestyle data in ways that help the health care industry shift its focus from providing sick care to partnering with people (rather than "patients") to help them stay well. The government and private sector have dedicated enormous capital and energy to building electronic health record and claims systems to automate and record sick-care transactions. This digitization supports consistent quality of care and payment accuracy, but the data is primarily retrospective – it tells a story of what has been.
Each year at HIMSS, the conference seems to surpass itself--in attendance as well as in the enrichment of knowledge around the halls. This year, among the roughly fifty thousand attendees, I enjoyed conversations with several fellow healthcare professionals around a variety of industry topics. Among them, these three trends emerged as the most valuable for improving care in today's digital environment. Traditionally, the healthcare industry--providers and payers alike--have been less adventurous with AI technologies than those in other fields. Now, after years of focusing on electronic medical records (EMRs) and connecting various sources of big data, many providers have reached a level of maturity to consider the implementation of AI.
And we may not even know it. As a cohort of technologies that lets machines solve problems and execute tasks formerly reserved for humans, AI drives everything from smartphone location data to flagging email span. The power of AI starts with large data sets, something that's become more evident in healthcare. Automated patient records, information sharing across entities and the full digitization of business operations has ushered in the era of big data. Providers now are only beginning to determine how to use this new technology to create efficiencies, while also maintaining and improving the patient experience.