Machine learning is a part of everyday life for most Americans, from navigation apps to Amazon's omniscient purchase recommendations. But in healthcare the use of machine learning has so far been limited to niche science projects in large and academic health systems – those able to afford the highly skilled data scientists and dedicated teams required to turn their data into meaningful performance improvements. Health Catalyst is on a mission to change that by embedding the value of machine learning throughout healthcare. Last month, the company launched healthcare.ai to help make machine learning routine, pervasive and actionable for healthcare organizations of all sizes. The collaborative, open source repository of machine learning tools and expertise including topical blog content and weekly live hands-on machine learning educational broadcasts, makes it easy to deploy machine learning in any environment.
The promise of AI in healthcare is finally starting to move beyond speculation. In recent years companies have been funneling funds into advancements, especially those that cut costs and promote patient health. Spending on healthcare AI technology is expected to surpass $34 billion by 2025, compared to $2.1 billion in 2018, according to market intelligence firm Tractica. Amazon, Siemens, IBM, Optum and GE Healthcare and health systems Mayo Clinic, Memorial Sloan Kettering and Intermountain are mining patient records for health data to train AI algorithms, allowing the machines to learn by recognizing patterns and make key predictions. In some cases, such deep learning systems are already outperforming doctors.
Healthcare is one of the major success stories of our times. Medical science has improved rapidly, raising life expectancy around the world, but as longevity increases, healthcare systems face growing demand for their services, rising costs and a workforce that is struggling to meet the needs of its patients. Demand is driven by a combination of unstoppable forces: population aging, changing patient expectations, a shift in lifestyle choices, and the never-ending cycle of innovation being but a few. By 2050, one in four people in Europe and North America will be over the age of 65--this means the health systems will have to deal with more patients with complex needs. Managing such patients is expensive and requires systems to shift from an episodic care-based philosophy to one that is much more proactive and focused on long-term care management.
As the efficacy of artificial intelligence (AI) in improving aspects of healthcare delivery is increasingly becoming evident, it becomes likely that AI will be incorporated in routine clinical care in the near future. This promise has led to growing focus and investment in AI medical applications both from governmental organizations and technological companies. However, concern has been expressed about the ethical and regulatory aspects of the application of AI in health care. These concerns include the possibility of biases, lack of transparency with certain AI algorithms, privacy concerns with the data used for training AI models, and safety and liability issues with AI application in clinical environments. While there has been extensive discussion about the ethics of AI in health care, there has been little dialogue or recommendations as to how to practically address these concerns in health care. In this article, we propose a governance model that aims to not only address the ethical and regulatory issues that arise out of the application of AI in health care, but also stimulate further discussion about governance of AI in health care. Interest in AI has gone through cyclical phases of expectation and disappointment since the late 1950s because of poor-performing algorithms and computing infrastructure.1 However, the emergence of appropriate computing infrastructure, big data, and deep learning algorithms has reinvigorated interest in artificial intelligence (AI) technology and accelerated its adoption in various sectors.2 While recent approaches to AI, such as machine learning, have only been relatively recently applied to health care, the future looks promising because of the likelihood of improved healthcare outcomes.3,4
Artificial Intelligence (AI) is getting increasingly sophisticated day by day in its application, with enhanced efficiency and speed at a lower cost. Every single sector has been reaping benefits from AI in recent times. The Healthcare industry is no exception. Here is decoding the future trajectory of healthcare with AI. The impact of artificial intelligence in the healthcare industry through machine learning (ML) and natural language processing (NLP) is transforming care delivery.