By Sarai Rodriguez
Startup Rhino Health recently joined the Alliance of Artificial Intelligence in Healthcare (AAIH), a consortium whose goal is to establish responsible, standards for the development, implementation, and use of artificial intelligence (AI) and machine learning (ML) technology.
The AAIH, which was founded in 2018, contains over 40 organizations, including global leaders such as Amazon, Bayer, and GE Healthcare, dedicated to this mission, the press release stated.
“For AI to have strong, equitable, replicable, and continuously improving performance in healthcare, access to data is key,” co-founder, and CEO of Rhino Health, Ittai Dayan, MD, said in a public statement.
Rhino Health is a distributed computer platform, utilizing privacy-preserving federated learning technology to improve healthcare AI solutions. The platform offers medical researchers and healthcare AI developers access to diverse and disparate datasets and allows them to use it to create better AI algorithms, the startup stated.
Rhino Health says federated learning reduces the risk associated with moving and managing vast volumes of data. Instead of centralizing data in a central server, federated learning allows patient data to remain on-premises in the hospital.
“Federated data management, computation, and learning provides healthcare access without risking patient privacy,” Dayan said. “We look forward to contributing our knowledge of distributed computation and federated learning to this esteemed group of organizations to collaboratively advance AI in healthcare.”
As a new member of AAIH, Rhino Health will contribute to ongoing AI/ML data projects focusing on unmet needs. In addition, the company will participate in healthcare industry events, whitepapers, public consultations, and other thought leadership opportunities.
“Artificial Intelligence is now a staple in our world, from our daily lives as consumers to improving medicines and our overall health,” CEO of Cyclica and chairperson of AAIH, Naheed Kurji, said. “All of our members have come together to work pre-competitively towards this common goal to ensure the fair and ethical use of AI to transform patient care.”
Researchers and clinicians have applied AI and ML algorithms to everything from chronic disease management to mental healthcare and medical imaging.
Even though implementing AI in healthcare can be transformative, it is vital to proceed with caution and balance healthcare AI.
Data models need wider adoption to support AI tool development, deployment, and maintenance, a National Academy of Medicine report found.
“The wisest guidance for AI is to start with real problems in healthcare, explore the best solutions by engaging relevant stakeholders, frontline users, patients, and their families, and implement and scale the ones that meet our Quintuple Aim: better health, improved care experience, clinician well-being, lower cost, and health equity throughout,” the report stated.
