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clinical decision support


Artificial Intelligence-based Clinical Decision Support for COVID-19 -- Where Art Thou?

arXiv.org Artificial Intelligence

Prior to January 2020, the artificial intelligence and machine learning (AI/ML) for healthcare community had many reasons to be pleased with the recent progress of their field. Learning-based algorithms had been shown to accurately forecast the onset of septic shock [1], MLbased pattern recognition methods classified skin lesions with dermatologist level accuracy [2], diagnostic AI systems successfully identified diabetic retinopathy during routine primary care visits [3], AIbased breast cancer screening outperformed radiologists by a fairly large margin [4], MLdriven triaging tools improved outcome differentiation beyond the emergency severity index [5], AIenabled assistance systems simplified interventional workflows [6], and algorithm-driven organizational studies enabled redesign of infusion centers [7]. Many would have argued that, after nearly 60 years on the test bench [8], AI in healthcare had finally reached a level of maturity, performance, and reliability that was compatible with the unforgiving requirements imposed by clinical practice. Today, only a few months later, this rather sunny outlook has become overcast. The worlds healthcare systems are facing the outbreak of a novel respiratory disease, COVID-19.


How AI and machine learning are transforming clinical decision support

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"Between 12 to 18 million Americans every year will experience some sort of diagnostic error," said Paul Cerrato, a journalist and researcher. "So the question is: Why such a huge number? And what can we do better in terms of reinventing the tools so they catch these conditions more effectively?" Cerrato is co-author, alongside Dr. John Halamka, newly minted president of Mayo Clinic Platform, of the new HIMSS Book Series edition, Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning. At HIMSS20, the two of them will discuss the book, and the bigger picture around CDS tools that are fast being transformed by the advent of artificial intelligence, machine learning and big data analytics.


The Impact Of Artificial Intelligence In Healthcare 2020

#artificialintelligence

When we think about AI, we have an image of robots in our minds. Some of them are creating chaos, and others are busy buzzing lullabies. Truth be told, AI is not just involved with robotics. It is more than that. As in a human body, the brain is a vital organ that controls and coordinates actions and reactions performed by us.


Today initial diagnosis comes with high levels of accuracy: Dr. John Danaher - ET HealthWorld

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Shahid Akhter, editor, ETHealthworld spoke to Dr. John Danaher, President, Clinical Solutions, Elsevier, to know what role artificial intelligence plays in healthcare and how Elsevier plans to improve diagnostic outcomes by way of AI and machine learning. Clinical errors and role of AI and health analytics There are three examples. The first one is making an initial diagnosis. What can be achieved with artificial intelligence, machine learning and actual language processing is the ability to assist doctors to make more accurate initial diagnosis. Second is the work being done in the area of image recognition with radiology and pathology.


AI and machine learning are changing our approach to medicine and the future of healthcare

#artificialintelligence

Artificial Intelligence (AI) is commonly known for its ability to have machines perform tasks that are associated with the human mind - like problem solving. However, what's less understood is how AI is being used within specific industries, such as healthcare. The healthcare industry continues to evolve as machine learning and AI in technology become more popular in the digital age. Business Insider Intelligence reported that spending on AI in healthcare is projected to grow at an annualized 48% between 2017 and 2023. Machine learning has the potential to provide data-driven clinical decision support (CDS) to physicians and hospital staff - paving the way for an increased revenue potential.



AI IN MEDICAL DIAGNOSIS: How top US health systems are reacting to the disruptive force of AI by revolutionizing diagnostic imaging, clinical decision support, and personalized medicine

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AI is rocking medical diagnosis with its potential to incite drastic improvements to hospital processes. AI can process images and patient health records with more accuracy and expediency than humans are capable of, lessening physician workload, reducing misdiagnosis, and empowering clinical staff to provide more value. While early moving hospitals are already extracting value from AI in medical diagnosis, most US hospitals are at the very early stage of the AI transformation curve -- and they risk falling behind if they don't move now. In this report, Business Insider Intelligence examines the value of AI applications in three high-value areas of medical diagnosis -- imaging, clinical decision support, and personalized medicine -- to illustrate how the tech can drastically improve patient outcomes, lower costs, and increase productivity. We look at US health systems that have effectively applied AI in these use cases to illustrate where and how providers should implement AI.


AI and machine learning are changing our approach to medicine and the future of healthcare

#artificialintelligence

Artificial Intelligence (AI) is commonly known for its ability to have machines perform tasks that are associated with the human mind - like problem solving. However, what's less understood is how AI is being used within specific industries, such as healthcare. The healthcare industry continues to evolve as machine learning and AI in technology become more popular in the digital age. Business Insider Intelligence reported that spending on AI in healthcare is projected to grow at an annualized 48% between 2017 and 2023. Machine learning has the potential to provide data-driven clinical decision support (CDS) to physicians and hospital staff - paving the way for an increased revenue potential.


AI and machine learning are changing our approach to medicine and the future of healthcare

#artificialintelligence

Artificial Intelligence (AI) is commonly known for its ability to have machines perform tasks that are associated with the human mind - like problem solving. However, what's less understood is how AI is being used within specific industries, such as healthcare. The healthcare industry continues to evolve as machine learning and AI in technology become more popular in the digital age. Business Insider Intelligence reported that spending on AI in healthcare is projected to grow at an annualized 48% between 2017 and 2023. Machine learning has the potential to provide data-driven clinical decision support (CDS) to physicians and hospital staff - paving the way for an increased revenue potential.


DC3 -- A Diagnostic Case Challenge Collection for Clinical Decision Support

arXiv.org Artificial Intelligence

In clinical care, obtaining a correct diagnosis is the first step towards successful treatment and, ultimately, recovery. Depending on the complexity of the case, the diagnostic phase can be lengthy and ridden with errors and delays. Such errors have a high likelihood to cause patients severe harm or even lead to their death and are estimated to cost the U.S. healthcare system several hundred billion dollars each year. To avoid diagnostic errors, physicians increasingly rely on diagnostic decision support systems drawing from heuristics, historic cases, textbooks, clinical guidelines and scholarly biomedical literature. The evaluation of such systems, however, is often conducted in an ad-hoc fashion, using non-transparent methodology, and proprietary data. This paper presents DC3, a collection of 31 extremely difficult diagnostic case challenges, manually compiled and solved by clinical experts. For each case, we present a number of temporally ordered physician-generated observations alongside the eventually confirmed true diagnosis. We additionally provide inferred dense relevance judgments for these cases among the PubMed collection of 27 million scholarly biomedical articles.