In early April, Anap (the French national agency for supporting the performance of healthcare and medico-social institutions) announced the launch of a national platform to share artificial intelligence solutions deployed in healthcare institutions. It is possible to consult, for each of the referenced solutions, its history, its level of maturity, the number of users who benefit from it and the key success factors identified. The agency invites healthcare institutions to share their AI projects. Anap, a public agency of expertise attached to the Ministry of Health, has the mission to respond to the needs of health and medico-social institutions through actions (methods, tools, events, interventions) developed with and for professionals. Healthcare professionals use AI solutions (to help them diagnose and choose treatments, predict patient flows or automate tasks) to improve care, patient experience and the internal organization of facilities.
In this video, Rohin Francis, MBBS, reviews modern health trends and the dangers of unsupported medical claims. The following is a transcript of this video; note that errors are possible. Francis: There is so much that I could say about the collision of the worlds of Silicon Valley and the mindset that drives it, sometimes referred to as the "tech bros," with the world of medicine and the strange bedfellows that they make. I will explore many of the phenomena that arise when this happens in future videos, things like novelty bias, where you assume that something new must be better. While this normally holds true for computing and we're all familiar with Moore's law, it very commonly isn't true in medicine, with many new and exciting therapies being quietly or occasionally loudly shelved years later for being useless or worse, harmful, or how Silicon Valley's motto of "move fast and break things" can be catastrophic for medicine. If you want to hear more about tech and medicine, then please do consider subscribing. But for this video, I want to focus on one aspect, the obsession with data. The belief that if we just measure more and more we can unlock the secrets of the human body. We can use 100% of our brain, become immortal, and transform into supernatural beings comprised of pure energy.
Self-supervised learning (SSL) is gaining a larger foothold in the world of machine learning (ML). As learning models are refined and expanded, machines that teach themselves, understand context and are able to fill in the blanks where there are holes in the information are the next step. Machines are taught to analyze, predict and advise on possible outcomes. Supervised learning - Practitioners train the machine on inputs paired with labelled outputs, teaching it to make associations. Example: A shape with three sides is labelled triangle .
Robotic surgery is finally taking shape in healthcare. Advances in robotics technology have been adapted in various subspecialties of both open and minimally invasive surgery, offering benefits such as enhanced surgical precision and accuracy with reduced fatigue of the surgeon. MIT engineers have developed a telerobotic system to help surgeons quickly and remotely treat patients experiencing a stroke or aneurysm. With a joystick, surgeons in a hospital can control a robotic arm at another location to safely operate during a critical window of time that could save the patient's life and preserve their brain function. The new system consists of a medical-grade robotic arm with a magnet attached to its wrist and sits beside the patient's head as they lie on an operating table at their local hospital.
In health care, the days of business as usual are over. Around the world, every health care system is struggling with rising costs and uneven quality despite the hard work of well-intentioned, well-trained clinicians. Health care leaders and policy makers have tried countless incremental fixes--attacking fraud, reducing errors, enforcing practice guidelines, making patients better "consumers," implementing electronic medical records--but none have had much impact. At its core is maximizing value for patients: that is, achieving the best outcomes at the lowest cost. We must move away from a supply-driven health care system organized around what physicians do and toward a patient-centered system organized around what patients need. We must shift the focus from the volume and profitability of services provided--physician visits, hospitalizations, procedures, and tests--to the patient outcomes achieved. And we must replace today's fragmented system, in which every local provider offers a full range of services, with a system in which services for particular medical conditions are concentrated in health-delivery organizations and in the right locations to deliver high-value care. Making this transformation is not a single step but an overarching strategy. We call it the "value agenda." It will require restructuring how health care delivery is organized, measured, and reimbursed. In 2006, Michael Porter and Elizabeth Teisberg introduced the value agenda in their book Redefining Health Care. Since then, through our research and the work of thousands of health care leaders and academic researchers around the world, the tools to implement the agenda have been developed, and their deployment by providers and other organizations is rapidly spreading. The transformation to value-based health care is well under way. Some organizations are still at the stage of pilots and initiatives in individual practice areas. Other organizations, such as the Cleveland Clinic and Germany's Schön Klinik, have undertaken large-scale changes involving multiple components of the value agenda. The result has been striking improvements in outcomes and efficiency, and growth in market share. There is no longer any doubt about how to increase the value of care. The question is, which organizations will lead the way and how quickly can others follow? The challenge of becoming a value-based organization should not be underestimated, given the entrenched interests and practices of many decades. This transformation must come from within.
Remote robotic-assisted surgery is far from new, with various educational and research institutions developing machines doctors can control from other locations over the years. There hasn't been a lot of movement on that front when it comes to endovascular treatments for stroke patients, which is why a team of MIT engineers has been developing a telerobotic system surgeons can use over the past few years. The team, which has published its paper in Science Robotics, has now presented a robotic arm that doctors can control remotely using a modified joystick to treat stroke patients. That arm has a magnet attached to its wrist, and surgeons can adjust its orientation to guide a magnetic wire through the patient's arteries and vessels in order to remove blood clots in their brain. Similar to in-person procedures, surgeons will have to rely on live imaging to get to the blood clot, except the machine will allow them to treat patients not physically in the room with them.
In a high-tech lab on Johns Hopkins University's Homewood campus in Baltimore, engineers have been building a robot that may be able to stitch back together the broken vessels in your belly and at some point maybe your brain, no doctor needed. The robot has a high-tech camera on one arm and a high-tech sewing machine on a second arm. "It's like park assist in a car," said Axel Krieger, an assistant professor of mechanical engineering in Hopkins' Whiting School of Engineering. This kind of suturing is performed more than a million times a year in surgeries around the country, said Krieger, part of a team developing the robot and senior author on a recent paper describing the technology in science robotics. The goal is to develop in the next several years a robot that makes the intricate and delicate work of suturing more consistent.
Heart transplantation can be a lifesaving operation for patients with end-stage heart failure. However, many patients experience organ transplant rejection, in which the immune system begins attacking the transplanted organ. But detecting transplant rejection is challenging—in its early stages, patients may not experience symptoms, and experts do not always agree on the degree and severity of the rejection. To help address these challenges, investigators from Brigham and Women's Hospital created an artificial intelligence (AI) system known as the Cardiac Rejection Assessment Neural Estimator (CRANE) that can help detect rejection and estimate its severity. In a pilot study, the team evaluated CRANE's performance on samples provided by patients from three different countries, finding that it could help cardiac experts more accurately diagnose rejection and decrease the time needed for examination. Results are published in Nature Medicine.