How Amazon Plans to Use Machine Learning for Innovative MedTech
In mid-October, Amazon hosted a conference about internal machine learning. This robust form of artificial intelligence (AI) has already made waves in the industry, from image recognition to wildlife preservation. Amazon scientists and engineers put their minds together at this conference to help revolutionize the future of MedTech. This continues their growing involvement in telehealth and medicine, including the recent launch of Amazon Clinic (replacing Amazon Care as it closes its doors).
Learn about their futuristic solutions here, exploring the different healthcare fields that they envisioned changes for with the power of machine learning (ML).
"The future of this technology will lead to AI models coming up with their own solutions in drug discovery."
1.) Drug development and testing.
Sergey Menis, a scientist and Senior Solutions Architect at Amazon, contributed to HIV vaccine development by designing a nanoparticle in May 2022 that interacts with a certain protein to encourage the production of rare antibodies. His presence at the Amazon Machine Learning Conference led to discussions about drug discovery with ML tools. For example, methods to improve drug development and tests with ML models for proteins, medications, and diseases.?
At the cutting edge of it all is the AI program, AlphaFold , from the DeepMind research lab owned by Google’s parent company. Presently, this medical advancement is improving research capabilities across the biology industry by providing accurately predicted 3D models of protein structures. Menis speculated that the future of this technology will lead to AI models coming up with their own solutions in drug discovery. A similar feature exists in Deep Learning , called inpainting. In this process, Deep Learning tools rebuild missing parts of an image or video. Selecting the necessary protein for drug development would resemble inpainting in a MedTech environment.
2.) Genomics and clinical trials.
Taha Kass-Hout is at the forefront of genomics and life sciences . As the Chief Medical Officer and Director of Machine Learning at AWS, he had lots to cover. His pursuit of next-generation data sequencing would apply to genomics, proteins, and small molecules. Data sequencing allows medical professionals to determine a living being’s full genetic makeup, which can help find differences within the genome. Seeing those distinctions in sequence modeling through ML could improve how scientists study the ways that certain diseases work.?
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Menis also had insights to share here, suggesting the possibility of clinical trials carried out in AWS with “an accurate digital twin of a human and their environment” powered by machine learning. This would make clinical trials much faster and easier to design, getting drugs to the public sooner and safely. Traditional clinical trials can go for any length of time, but they typically take about 10 years—and only 10% to 20% succeed.
"MedTech development has high quality standards and lengthy development timelines."
3.) Overcoming barriers to adoption for predictive health ML.
As with most innovations, the biggest hurdles are time and cost to produce results. The lightbulb took over 1 year to invent, the first plane took 4 years, and the internet took 40 years. MedTech development has high quality standards and lengthy development timelines. Because of this, it will also require significant levels of commitment and resources from Amazon and other competitors looking to lead the field.
While Amazon’s team is pioneering a world where MedTech development is more accessible, our team here at Devsu has made it simple to find machine learning experts fast. We believe in the bright future of technology, and we want to help you achieve your goals with our pre-vetted talent pool. Launch products that will reshape the industry as we know it by getting started with your digital projects today.
Written by: Jaclyn Blute