Using Zero Trust Technology to Expedite Healthcare AI Innovation

AI has the potential to lower costs by up to 50% while improving results for several treatments by 30 to 40% across the board, from research to diagnosis to therapy. Even though the market for healthcare algorithms is expected to reach $42.5 billion by 2026, the FDA has only authorized less than 35 algorithms, and only two of those are considered to be truly new. Historically, obtaining the huge data sets required for generalizability, openness, and bias reduction has been challenging and time-consuming, largely because of regulatory limitations put in place to safeguard patient data privacy. Integrating Zero Trust technology with artificial intelligence allows for secure collaborations between data stewards (such as health systems, etc.) and algorithm owners. This eliminates the requirement to anonymize or de-identify Protected Health Information (PHI) because the data is never accessible or visible.

AI with Zero Trust Technology Enables Excellent Healthcare

Artificial intelligence (AI) and machine learning can assist healthcare practitioners in providing better care, doing their jobs more efficiently, and spending less money. For instance:

●The progression of severe disease in COVID-19 patients was accurately predicted using AI analysis of chest x-rays.
●Up to 5 years in advance, a deep learning algorithm based on images that were built at MIT can forecast breast cancer.
●The first algorithm built in a medical device to receive FDA approval can identify pneumothorax (collapsed lung) from CT scans, helping to identify and treat people with this life-threatening illness. This algorithm was created at the University of California, San Francisco.

The uptake of clinical AI has also been gradual. In 2019, alone, more than 12,000 papers in the life sciences discussed AI and machine learning. However, in the U.S. Only a small number of AI- and machine learning-based medical technologies have received FDA approval so far. Data availability is a significant roadblock to clinical approval. The FDA demands evidence that a model is generalizable, meaning that it will function correctly regardless of the patients, settings, or machinery. For the algorithm to run against all the variables it will encounter in the actual world, this standard requires access to extremely diverse, real-world data. However, access to such data is restricted due to privacy regulations and security concerns.

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