The US healthcare industry saved $300 B per year by using machine intelligence. AI has already improved diagnosis accuracy, reduced required time to determine cause and course of the disease, and helped in formulating an effective treatment for diseases. The majority of these systems are trained with private and sensitive medical data and some are used to make critical health-related decisions. Therefore, healthcare-AI systems should be resistant against differential privacy and inference attacks and should process data with utmost fairness and transparency. Our AI-Security scientists may closely work with data collectors, solution developers, and customers in the healthcare industry to assure the safety and privacy of their AI engines and their compliance with security standards and regulatory requirements.

  • Security Review of healthcare-AI Systems: privacy, security, and fairness risks should be considered throughout the Healthcare-AI data pipeline, from data collection and solution development to deploying and using AI systems. Making sure that collected data are not biased and fairly represent different groups is the first step in building a reliable AI engine. To be trustable, healthcare-AI systems should offer a very high degree of transparency and explainability without risking the privacy of data that is used to train those systems.

Our AI Cybersecurity Review service provides data collectors, solution developers, service employers, and consumers in the healthcare sector with an independent view of current security and privacy issues in their AI services.

  • AI Privacy Risk Assessment and Security Compliance Review of Smart Transportation Systems: Most of the medical data constitutes sensitive information and inappropriately sharing and using it may damage the privacy of patients. Considering strict privacy requirements in the healthcare sector, it is extremely important to address privacy risks in data collection, training, testing, and deploying AI engines.

Our AI-Security scientists work closely with AI solution developers and customers to ensure all privacy and regulatory requirements are met during data collection, system development, and in day-to-day use of healthcare-AI systems.