AI and Medical Diagnosis

Opportunities for AI to assist Physicians are everywhere

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This future is pretty close. In some areas of healthcare, the future is already here.

Kang Zhang at the University of California in San Diego and his colleagues trained an AI on medical records from 1.3 million patient visits at a major medical center in Guangzhou, China. The AI is able to diagnose glandular fever, roseola, influenza, chicken pos and hand-foot-mouth disease with 90 and 97 percent accuracy.

With speedy innovation, there are also challenges.

Technology Behind AI for Diagnosis

These AI Systems for diagnosis uses deep learning techniques to arrive at a diagnosis. These systems source image data and symptoms data from healthcare facilities to train on. Then, the systems apply the techniques to arrive at the diagnosis. The problem with deep learning techniques is transparency. Although the inputs and the outputs to the AI systems are transparent, the way that the AI systems arrive at the diagnosis decision is unclear. The system is also dependent on the quality of the data to ensure accuracy.

Challenges of Sourcing Data

In the U.S., healthcare data is located in various healthcare facilities, at insurance companies, and inside government agencies. Sourcing all of the data into a data cloud for AI Systems to use is a huge task. Google is currently embarked on building this exact infrustructure for medical technology companies and healthcare facilities to use to train their AI Systems. It is taking significant amount of investment for Google to step through all the regulations and privacy rules to provide this infrastructure.

Challenges aside, the future looks bright for AI in medical diagnosis.

Medical Imaging

One of the newest areas for using AI Systems to both automate the workflow for efficiency as well as assisting in diagnosis is in Medical Imaging.

Conclusion

As AI innovations progress in healthcare, it’s critical for both medical technology companies, data providers (Google), hospitals, governments, and insurance companies to work together to foster an atmosphere of innovation. In this atmosphere of innovation, better opportunities for automation can be identified, proper data can be sourced, thus lead to better accuracy in both medical diagnosis and better efficiency in the healthcare workflow.

Writer, Technologist: Tech|Future|Leadership (Forbes-AI, Behind the Code)

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