Data Complexities Abound

While the possibilities of AI in transforming healthcare are almost endless, Indian healthcare providers must deal with regulatory concerns or more precisely, the absence of regulation in the private sector according to the market research. The complex and variable nature of healthcare data, along with growing ethical and privacy issues, makes it highly challenging to build AI algorithms.

Another critical challenge, says Gupta of Suki, is around interoperability. AI requires vast datasets to operate, so enabling systems to share data will result in richer, more comprehensive datasets to power AI solutions.

“At the same time, healthcare providers must appreciate the importance of data security and privacy, and permissions and protocols must be put in place to guarantee that people only have access to the information they need to do their jobs. To reduce the risk of bad actors, infrastructure must be developed. Finally, it’s critical to communicate to the market exactly what data can and cannot be used for,” he adds.

Furthermore, because AI-generated models can be difficult to explain, doctors now have the additional burden of understanding and having confidence in recommendations from AI-powered solutions. For clinical recommendations, regulatory oversight should be heavily considered to help ensure the safety of these solutions.

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