Many people predict that artificial intelligence and machine learning (AI/ML) will eventually enable better, more precise and earlier disease treatment, and engage patients more efficiently and cost-effectively than our current healthcare system. AI/ML refers to the human-like capabilities of specific mathematical algorithms processed by computers. It refers to software applications that can learn through machine learning and deep learning algorithms.
The goal of AI in healthcare is to emulate human cognition and analyze relationships between prevention or treatment techniques and patient outcomes. Then machine learning in healthcare could predict illness and treatment so physicians can intervene earlier, predict population health risks by identifying patterns and high-risk markers and even model individual disease progression.
Machine learning already plays a key role in many health-related areas, including developing new medical procedures, handling patient data records and treating chronic diseases. Machine learning has virtually endless applications in healthcare, where it is already helping to streamline administrative processes in hospitals, map and treat infectious diseases such as CoVid19 and personalize medical treatments. Machine learning’s impact on healthcare affects these four major areas:
One thing our healthcare systems have in abundance is data. Yet, due to different storage systems, privacy concerns and no established process for easily sharing data, major data analyses are not currently being done. ML could provide tremendous improvements for patients, doctors and healthcare organizations. According to one estimate, big data could save medicine and the pharmaceutical industry up to $100B annually thanks to improved efficiencies in clinical trials and research, better insights for decision-making and new tools that will help physicians and consumers make better health decisions.
AI’s influence on helping us monitor and predict health epidemics has already been shown. In one case, a computer algorithm identified an Ebola outbreak nine days before the World Health Organization reported it. The computer studied social media sites, news reports and government websites to identify this outbreak. Although current efforts to identify outbreaks using AI algorithms are imperfect, they have huge potential.
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We reviewed our surgical outcome data to predict the degree of visual acuity improvement based on the pre-operative visual acuity.
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We are developing a new technique based on angiogram studies to assess why some eyes seem to respond more readily to treatment than others. Work is ongoing to provide clinical value to these methods.
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