Despite the fact that health in general globally is facing several challenges including antibiotic resistance, lack of accessible and affordable health care, rise of chronic and untreatable diseases such as dementia, there is a ray of hope in the advent of technology in healthcare.
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What is most rewarding and holds potential is advent of Artificial Intelligence (AI) in healthcare and diagnostics. Ada Health co-founder Claire Novorol says that they make Ada which is a diagnostic AI built with GPs. It can help make diagnosing several conditions easier and faster.
Federal National Institute of Standards and Technology (NIST) and the Academy for Radiology and Biomedical Imaging Research (ARBIR) last week hosted a workshop on AI in medical imaging. They invited multidisciplinary experts to build guidelines that could help shape role of AI in healthcare.
According to scientists Denis Bergeron and Michael Garris, AI would mean uniting medical imaging with computer science and developing it to precision. This means that it would be able to “calibrate disparate measurements” and also reduce the bias between operators. This means that the tests prescribed in future would be free of hassles and would be performed accurately. Bergeron and Garris wrote in a NIST blog, “Physical measurement standards will ensure that the data these tests generate will mean the same thing across patients, over time, and when measured using devices from different manufacturers.”
There is a rush to adopt electronic medical record (EMR) in healthcare but that is not part of AI. Experts believe that the NIST needs to go a long way before their precise and effective AI is a reality. The meeting last week in Gaithersburg, Maryland, was an effort to take this forward and incorporate it into real life clinical practice. The team has set its goal to create a “diagnostic cockpit” where the clinicians could go to access crucial data that could help them diagnose their patients more accurately with the help of AI. The team involves medical specialists, radiologists, pathologists, device manufacturers, data scientists as well as government officials.
This latest workshop is a follow up to the proceedings from last year where the NIST had begun visualizing the way forward with imaging rules, data formats and handling, performance guidelines and interfaces. They had started with AI and medical imaging for various diseases such as coronary artery disease and breast cancer to test its feasibility and utility in real-life scenarios.
According to experts, combining medical technology with diagnostics can improve precision medicine and diagnostics as well as improve value based care. Bergeron and Garris wrote, “Standardized data at scale will fuel machine learning and create new generations of analytic and diagnostic models… With AI’s ability to perform millions of incredibly complex weighing and correlation-finding calculations in a short period of time, human diagnostic teams will be able to quickly identify patterns and associations that they would otherwise miss.”
References
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