Artificial intelligence (AI), Machine learning, NLP, Robotics, and Automation are increasingly prevalent in all aspects and are being applied to healthcare as well. These technologies have the potential to transform all aspects of health care from patient care to the development and production of new experimental drugs that can have a faster roll-out date than traditional methods. There are numerous research studies suggesting that AI can outperform humans at key healthcare tasks, such as diagnosing ailments. Here is a great example, AI'outperforms' doctors diagnosing breast cancer¹. Artificial intelligence is a collection of technologies that come together form artificial intelligence. Tech firms and startups are also working assiduously on the same issues.
Healthcare is an important industry which offers value-based care to millions of people, while at the same time becoming top revenue earners for many countries. Today, the Healthcare industry in the US alone earns a revenue of $1.668 trillion. The US also spends more on healthcare per capita as compared to most other developed or developing nations. Quality, Value, and Outcome are three buzzwords that always accompany healthcare and promise a lot, and today, healthcare specialists and stakeholders around the globe are looking for innovative ways to deliver on this promise. Technology-enabled smart healthcare is no longer a flight of fancy, as Internet-connected medical devices are holding the health system as we know it together from falling apart under the population burden.
Medical billing and coding have been undergoing many changes in recent years as the healthcare industry increases in complexity while the variety of treatments and procedures grow by the minute. The healthcare industry is in urgent need of a scalable solution that can process the vast amount of patient data without compromising speed and accuracy of the billing procedure. The use of artificial intelligence in the medical billing and coding industry can help healthcare organizations facilitate their billing procedures while minimizing costly errors. AI-driven technologies, such as machine learning and natural language processing (NLP), have the ability to interpret and organize a large amount of data quickly and accurately. For instance, an AI program can arrange data from different records into a logical timeline to make sense of disparate events, diagnoses, and procedures, minimizing coding and reporting errors.
In recent years, researchers have been developing machine learning algorithms for an increasingly wide range of purposes. This includes algorithms that can be applied in healthcare settings, for instance helping clinicians to diagnose specific diseases or neuropsychiatric disorders or monitor the health of patients over time. Researchers at Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital have recently carried out a study investigating the possibility of using deep reinforcement learning to control the levels of unconsciousness of patients who require anesthesia for a medical procedure. Their paper, set to be published in the proceedings of the 2020 International Conference on Artificial Intelligence in Medicine, was voted the best paper presented at the conference. "Our lab has made significant progress in understanding how anesthetic medications affect neural activity and now has a multidisciplinary team studying how to accurately determine anesthetic doses from neural recordings," Gabriel Schamberg, one of the researchers who carried out the study, told TechXplore.
Artificial intelligence is driving changes in almost every industry, healthcare included. The cost of healthcare has been rising rapidly for decades on end. Some studies have concluded that healthcare will account for over 20% of the GDP of the US by 2025. At the same time, healthcare professionals are working hard to treat the increasing number of patients with their high patient care expectations. Artificial intelligence could be the solution that the industry is desperately searching for.
Mashable's series Algorithms explores the mysterious lines of code that increasingly control our lives -- and our futures. In hospitals and health systems across the country, physicians sometimes use algorithms to help them decide what type of treatment or care their patients receive. These algorithms vary from basic computations using several factors to sophisticated formulas driven by artificial intelligence that incorporate hundreds of variables. They can play a role in influencing how a doctor assesses kidney function, if a mother should give birth vaginally once she's had a Cesarean section, and which patients could benefit from certain interventions. In a perfect world, the computer science that powers these algorithms would give clinicians unparalleled clarity about their patients' needs.
Electronic sensors and artificial intelligence could help health professionals monitor and treat vulnerable patients in ways that improve the outcome without invading privacy, Stanford University said in a statement. A team of researchers reviewed the field of'ambient intelligence' in healthcare - an interdisciplinary effort to create smart hospital rooms equipped with AI systems that can do a range of things to improve patient outcomes. AI tools can unobtrusively monitor impending health crises in the elderly. Devices could prompt in-home caregivers, remotely located clinicians and patients themselves to make timely, life-saving interventions. Sensors and AI can immediately alert clinicians and patient visitors when they fail to sanitize their hands before entering a hospital room.
The role of machine learning and artificial intelligence (AI) in the health care sector is rapidly expanding. This expansion may be accelerated by the global spread of COVID-19, which has provided new opportunities for AI prediction, screening, and image processing capabilities.1 Applications of AI can be as straightforward as using natural language processing to turn clinical notes into electronic data points or as complex as a deep learning neural network performing image analysis for diagnostic support. The goal of these tools is not to replace health care professionals, but to enable better patient experience and better inform the clinical decision-making process to improve the safety and reliability of clinicians. Clinicians and health systems using these new tools should be aware of some of the key issues related to safety and quality in the development and use of machine learning and AI. The performance of a chatbot on a shopping website poses little harm to users, but AI used in health care, particularly clinical decision supports or diagnostic tools, can have significant impact on a patient's treatment.
It manages our phones and homes, helps us navigate, and advises us what to watch, read, listen to, and buy. Soon it will transform our health, says trauma surgeon and data-science expert Rachael Callcut, MD, MSPH. There is a certain amount of bias that we, as humans, bring to any clinical scenario: Without even realizing it, we may look past critical pieces of information that could help our patients get better. AI, which is essentially a computer algorithm that learns from data, can uncover patterns that we can't see – either because of those biases or because the human brain simply can't assimilate the vast quantity of medical data that is now available from hospital sensors and other digital health devices. Ultimately, AI promises to reduce human error and make our care more efficient, which will improve outcomes for our patients.
Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype.1 Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems.2 For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results.3