Leveraging Zero Trust Technology to Accelerate Healthcare AI Innovation

What is Artificial Intelligence in Healthcare?

The use of machine learning (ML) algorithms and other intelligent computing in medical contexts are referred to as AI in healthcare.

AI can enhance clinical outcomes by 30 to 40% and cut costs by 50%, from studies to diagnosis to therapy. Although healthcare systems are expected to be worth $42.5 billion by 2026, the FDA has only authorized 35 programs, just two of which are new. Due to regulatory limits implemented to safeguard patient data privacy, obtaining large data sets required for generalization, openness, and bias reduction have traditionally been challenging and time-consuming.

How can Artificial Intelligence Help Healthcare?

AI and machine learning can assist healthcare professionals in improving treatment, increasing productivity and lowering costs by revealing vital insights into large amounts of data. Consider the following scenario:

● With a reasonable level of accuracy, AI analyses of chest x-rays forecasted the course of catastrophic illness in COVID-19 victims.
● Prostate cancer can be predicted up to five years ahead of time using an image-based deep learning approach created at MIT.
● The first algorithms built into a medical device to receive FDA approval were created at the University of California, San Francisco. They can identify pneumothorax (collapsed lung) from CT scans, enabling prioritize and help patients with this life-threatening illness.

At the same time, clinical AI has been reluctant to catch on. Over 12,000 papers in the life sciences described Machine learning and AI in just one year. Despite this, the FDA has only authorized a little more than 30 AI and machine learning-based medicinal products to date. A lack of data access hampers clinical approval. The FDA needs verification that a model is generalizable, meaning that it will operate consistently across patients, surroundings and equipment. This requirement necessitates access to a wide range of real-world data so that the algorithms can be trained on all of the parameters they will encounter in practice. However, such information is hard to obtain due to security and privacy issues.

Future of AI in Healthcare

The AI3C board of advisers consists of voluntary senior executives who will collaborate to co-create AI solutions for beneficial social and healthcare outcomes, develop and set AI strategy and vision for a range of initiatives, and assess AI adoption progress in the industry.

Every member organization has appointed its own AI advocates to act as regional leaders and program coordinators.

The AI3C’s goal is to use AI to tackle major commercial problems, such as:

● General industrial and economic issues - including knowledge transfer, compliance requirements and funding mechanisms.
● Organizational and cultural obstacles - Including labor regulations, all affect digital skills and employment prospects.
● Data security – including data access and shared innovation

The AI3C’s overall goal is to speed up AI development and adoption by:

● Showcase of new AI tools
● Obtaining AI application cases, best practices, and research input particular to the industry
● AI workforce transformation
● Students are being prepared for professions in AI and data science.

Solving the Challenges of Applications of AI in Healthcare

To attain these aims, AI3C representatives will concentrate on creating a systematic plan for sustainable AI training and up-skilling. The AI3C will connect with the health sector through white papers, new products and programs, and social media engagement to make AI highly relevant and effective. The alliance will hold quarterly events and conferences to openly share the solution and status of critical AI adoption obstacles.

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