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Artificial Intelligence in Healthcare: Growth is Slow But Incredible!

Artificial Intelligence (AI) and Machine Learning (ML) technologies are posing numerous thrilling opportunities for the healthcare sector.

These platforms are incredible in amplifying molecular research and development functions and for making accurate clinical decisions.

But, with continuously changing healthcare regulations, many investors are still in a dilemma to adopt these advanced technologies for optimizing their processes.

Through this blog, we tried to explain to healthcare service providers how to overcome such healthcare compliance issues to completely transform their business with AI.

This new policy comprises various guidance documents that label how the agency plans to regulate software that helps in clinical decision support (CDS).

To comply with the changing FDA approval processes, software developers must consider how to design and roll out their products in accordance with the FDA rules.

Our innovative AI-powered healthcare solutions for healthcare highly focused on patient safety, secure payment methods, and real-time communication.

Because, if your software is posing more negative outcomes, then the application will be banned in the market and the efforts you put on design, development, and getting approvals will be wasted in minutes.

But, if you feed the AI systems with wrong data, it will provide incorrect conclusions such as misdiagnosis and improper treatment recommendations.

Currently, many healthcare service providers are using artificial intelligent-based medical diagnostic devices to provide better services and optimize patient diagnosis process.

For instance, FDA approved AI-powered imaging diagnostic software/tools are helping clinicians in diagnosing and treating various health conditions such as cardiovascular disorders, diabetic conditions, and cancer.

The market researchers are estimating that the clinical health AI applications will save $150 billion per annum for the United States economy by 2025.

From clinical diagnosis and treatment to robotic surgery and drug development, AI plays an essential role in healthcare.

Yes, the complete rollout of AI will take years, but AI technology, ML and predictive analytics together bring an advanced healthcare solution that was never before available.

Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data

Data-driven Bayesian networks based analysis has been performed on health care data.Summarized, healthcare provider level data was used for this analysis.Novel hypothesis linking diagnosis codes was proposed based on findings from Bayesian networks approach.Potential mechanisms were explored to explain novel hypothesis.This paper demonstrates the ability of artificial intelligence methods to advance medical research.

ResultsWhile many interactions identified between discharge rates of diagnoses using this data set are supported by current medical knowledge, a novel interaction linking asthma and renal failure was discovered.

In summary, this study demonstrates that application of advanced artificial intelligence methods in healthcare has the potential to enhance the quality of care by discovering non-obvious, clinically relevant relationships and enabling timely care intervention.

Artificial intelligence has come to medicine. Are patients being put at risk?

Health products powered by artificial intelligence are streaming into our lives, from virtual doctor apps to wearable sensors and drugstore chatbots.

AI can help doctors interpret MRIs of the heart, CT scans of the head and photographs of the back of the eye, and could potentially take over many mundane medical chores, freeing doctors to spend more time talking to patients, said Dr. Eric Topol, a cardiologist and executive vice president of Scripps Research in La Jolla.

Even the Food and Drug Administration ― which has approved more than 40 AI products in the last five years ― says “the potential of digital health is nothing short of revolutionary.” Yet many health industry experts fear AI-based products won’t be able to match the hype.

Some doctors and consumer advocates fear that the tech industry, which lives by the mantra “fail fast and fix it later,” is putting patients at risk ― and that regulators aren’t doing enough to keep consumers safe.

And AI systems sometimes learn to make predictions based on factors that have less to do with disease than the brand of MRI machine used, the time a blood test is taken or whether a patient was visited by a chaplain.

In one case, AI software incorrectly concluded that people with pneumonia were less likely to die if they had asthma ― an error that could have led doctors to deprive asthma patients of the extra care they need.

Medical AI, which pulled in $1.6 billion in venture capital funding in the third quarter alone, is “nearly at the peak of inflated expectations,” concluded a July report from research company Gartner.

“As the reality gets tested, there will likely be a rough slide into the trough of disillusionment.” That reality check could come in the form of disappointing results when AI products are ushered into the real world.

Legislation passed by Congress in 2016 ― and championed by the tech industry ― exempts many types of medical software from federal review, including certain fitness apps, electronic health records and tools that help doctors make medical decisions.

And consumer advocates acknowledge that some devices ― such as ones that help people count their daily steps ― need less scrutiny than ones that diagnose or treat disease.

“It’s not the main concern of these firms to submit themselves to rigorous evaluation that would be published in a peer-reviewed journal,” said Joachim Roski, a principal at Booz Allen Hamilton, a technology consulting firm, and coauthor of the National Academy’s report.

“Nobody is going to be happy, including investors, if people die or are severely hurt.” The FDA has come under fire in recent years for allowing the sale of dangerous medical devices, which have been linked by the International Consortium of Investigative Journalists to 80,000 deaths and 1.7 million injuries over the last decade.

Many of these devices were cleared for use through a controversial process called the 510(k) pathway, which allows companies to market “moderate-risk” products with no clinical testing as long as they’re deemed similar to existing devices.

The FDA’s pilot “pre-certification” program, launched in 2017, is designed to “reduce the time and cost of market entry for software developers,” imposing the “least burdensome” system possible.

Scott Gottlieb said in 2017 while he was FDA commissioner that government regulators need to make sure its approach to innovative products “is efficient and that it fosters, not impedes, innovation.” Under the plan, the FDA would pre-certify companies that “demonstrate a culture of quality and organizational excellence,” which would allow them to provide less upfront data about devices.

Eventually, researchers realized the computer had merely learned to tell the difference between that hospital’s portable chest X-rays ― taken at a patient’s bedside ― with those taken in the radiology department.

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