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Auriga, a custom software development service provider, recently participated in the Artificial Intelligence (AI) Forum organized by Russia’s Trade Mission in France in a videoconference format. Being a CCI France Russie member, Auriga joined the event to expand its cooperation with French companies located in Russia and discuss AI trends with business leaders, researchers, and authorities of the two countries.

The AI Forum covered several AI application areas, including healthcare, manufacturing, environmental protection, urban planning, education, scientific research, and public administration. Auriga’s experts took part in a session devoted to AI technologies in healthcare, talking about the challenges and barriers to AI adoption in hospitals and clinics and how to overcome them with reasonable solutions.

AI vs. Hybrid Intelligence

In recent years, AI has been hyped as a game changer in many industries, including healthcare. At the same time, the revolutionary—and scary—thing is that AI is not an extension for humans but a substitution. AI does not help in analysis and decision making; it makes its own decisions. However, it is still hard to imagine that healthcare patients will want to entrust their lives and well-being to AI algorithms fully.

At the AI Forum, all speakers of the “AI in Healthcare” session agreed that AI is not the correct term to describe the solutions healthcare professionals, clinicians, and patients require today. It is rather hybrid intelligence (HI), which combines human and machine intelligence. Medical HI is intended not to replace physicians but, instead, to help them make better decisions faster and accelerate diagnostics by quickly analyzing digital patient data.

Playing Big with “Small Data”

In practice, HI-enabled solutions rely on AI subfields, including machine learning and computer vision. No one would underestimate these technologies’ potential, especially in healthcare, where they promise such benefits as treatment personalization and enhanced medical procedure efficiency. However, machine learning and computer vision heavily depend on input data, and healthcare is known for its challenges related to obtaining this data, including sufficiency, reliability, and security.

Auriga’s report at the AI Forum showcased our experience with efficiently training neural networks for medical tasks when accessible patient datasets are relatively small. Auriga’s speaker Andrey Teterin shared our ECG rhythm classification takeaways using insufficient labelled data for cardiac monitoring system development.

Summing Up

Andrey Shastin, Auriga’s Head of Global Business Development in the medical and embedded systems domains, said the following:

The AI Forum was a suitable platform for us to demonstrate how Auriga could help medical technology companies cope with some of the challenges of adopting emerging digital technologies in healthcare. In particular, we presented a solution to increase the reliability and efficiency of neural networks when labelled data is insufficient. Our experience shows that the healthcare community is more enthusiastic about machine learning and computer vision technologies than full-scale AI adoption.

Vyacheslav Vanyulin, Auriga’s CEO, added this:

Being a software development company experienced in not only the medical domain but various domains, Auriga offers the concept of seamless addition of AI-related methods and technologies to medical solutions of different classes. Full-scale AI adoption is still a thing of the future in such a delicate industry as healthcare.

Auriga boasts 18 years of medical software development and testing experience, and it has repeatedly been acknowledged by both customers and industry experts, such as the IAOP®. The company is ISO 13485 certified and runs eight R&D and testing labs for medical devices in Eastern Europe. Explore our online portfolio of medical device and digital health projects or contact us via the website to get more information on Auriga’s services.

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