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. Open issues related to clinical regulations are hampering the implementation of AI in healthcare.

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. Let’s start!

#1 Aware Of Regularity Frameworks

In the recent past, healthcare regulations in the United States are updating to keep up with the evolving digital healthcare market. The U.S. Food and Drug Administration (FDA) has been taking incremental phases to amendment the existing rules and regulations. Recently, it was introduced the “Digital Health Innovation Action Plan” to guide the agency’s role in advancing effective digital health technologies.

In addition, the FDA also focused on a digital health software Pre-Cert Pilot Program. It is enrolling software-as-a-medical-device (SaMD) developers in this project. This pilot project helps the FDA to determine the performance indicators that are needed for per-certifying the product. Using this, the FDA helps developers in identifying new ways of product approval procedures that are seamless.

Accordingly, another regularity framework, “Policy for Device Software Functions and Mobile Medical Applications” was introduced in 2019 for higher-risk software. This new policy comprises various guidance documents that label how the agency plans to regulate software that helps in clinical decision support (CDS). The CDS software identifies a patient’s medical conditions as it uses ML algorithms.

#2 Achieving FDA Approval

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. Particularly, developers should focus on achieving FDA approval for the software which comes under the higher risk category.

Here, artificial intelligence-powered diagnostic tools and applications come in place. The developers should be more focus on FDA rules when they develop AI-powered healthcare tools and software. As AI is evolving across all sectors, the healthcare industry is also investing in AI to automate its medical diagnostic processes.

Software Update is a major concern that developers face today. Because, based on the market requirements, the developers will update the existing software products. They will add new features to advance the functionalities using emerging AI and ML technologies. But, when they change the technical description of the software, the FDA approval status for the old version of the software will be at risk.

Similarly, organizations must know the company’s product development plans and approach that they follow to get FDA approvals. This provides investors a clear variation of the company over its competitors in the same industry.

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#3 Black-Box Nature of AI Is Hampering Its Growth In Clinical Applications

Yes, artificial intelligence is a black-box in its nature. This is one of the major drawbacks of the rapid adoption of AI-based applications in healthcare.

We’ll brief it out. Can all healthcare AI-powered applications track and assess the decision making procedure when a negative outcome happens? Will the set of trained data to the ML algorithms is visible to users? Will the reason for a negative outcome is identified by the technology itself?

The developers should focus on all the above points while designing an AI-powered software for healthcare. 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.

Accordingly, AI-systems need more data to perform the tasks that they assigned to do. But, if you feed the AI systems with wrong data, it will provide incorrect conclusions such as misdiagnosis and improper treatment recommendations. Error Detection algorithms help you out in this scenario. Thanks to technology developments.

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.

However, the adoption of these AI tools is sluggish in the market. There is a need to publish the AI’s benefits in healthcare. More awareness of artificial intelligence in healthcare to witness industry adoption and get the credibility of AI technology.

AI Opportunities in Healthcare

Implementation of AI in healthcare was at the pre-mature stage. Without significant investments, AI adoption in healthcare is slow and difficult. The market researchers are estimating that the clinical health AI applications will save $150 billion per annum for the United States economy by 2025.

Artificial intelligence in healthcare is used for multipurpose. AI can sense machines, learn, and perform both clinical and administrative tasks. Hence, health AI will augment every task done by humans intelligently and automatically. The graph below depicts the most promising healthcare applications of AI that are attracting more investments.

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On the global artificial intelligence healthcare market front, the investments are stunning and are increasing every year. Both the public and private sectors are heavily investing in health AI. Every healthcare company is in plans of implementing AI strategies to become competitive in the market. From clinical diagnosis and treatment to robotic surgery and drug development, AI plays an essential role in healthcare. Though the use of AI in healthcare is slow, health AI will completely change the structure of the existing healthcare market in the next decade.

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. According to researchers, compared to the clinical side, AI is going to use widely for automating admin and operational tasks.