Artificial intelligence (AI) needs lots of data to work well. In healthcare, AI can help with accurate diagnosis, predicting health outcomes, talking with patients, and managing tasks. But patient data is often stored in different places like hospitals and clinics. Laws like HIPAA limit how this protected health information (PHI) can be shared.
Omer Moran, an AI expert, says, “The biggest barrier to agentic AI is not a lack of data but the inability to use and share this data safely.” Agentic AI means AI systems that can make decisions on their own in real-time. These systems need data from many sources. Without safe ways to share this sensitive data, AI’s full potential in healthcare cannot be reached.
Homomorphic Encryption (HE) lets AI do calculations directly on encrypted data without needing to decrypt it first. This means patient information stays secret during AI processing. In simple terms, AI can analyze the encrypted data and give useful answers while keeping raw data hidden from others.
The benefits of HE include:
HE can require a lot of computing power, which can slow processing, especially when quick results are needed. Researchers are working to make HE faster and easier to use in more situations.
Trusted Execution Environments (TEEs) are special security areas inside modern computer processors, like Intel SGX, AMD SEV-SNP, and Arm TrustZone. They create safe spaces where data can be processed securely, separate from the main system. This keeps data safe even if other parts of the computer are hacked.
TEEs have important features like:
TEEs help in healthcare by letting AI work on data inside secure areas, keeping PHI private during analysis. Michal Wachstock from Duality Technologies says, “TEEs provide near-native performance compared to fully homomorphic encryption, making them practical for healthcare use cases.”
Using both Homomorphic Encryption and Trusted Execution Environments together makes healthcare AI more secure and private. They protect patient data at every step in the AI process.
This combined method supports AI workflows that follow HIPAA and other U.S. rules. It also lets hospitals and clinics work together to analyze data and improve patient care without sharing raw data outside their control.
Federated Learning (FL) is a way to train AI models using data stored in different places without moving the raw data. Local AI models are trained separately and then combined to make a better global model.
In healthcare, FL uses privacy tools like:
Research by K.A. Sathish Kumar and others shows some challenges in using FL in healthcare. These include handling different types of medical data, heavy communication needs, security risks, and following rules. TEEs and HE can lower computing costs and increase safety during training.
Duality Technologies uses FL to safely combine data from many healthcare sources while keeping privacy rules intact. This lets health providers work on AI projects without risking patient data privacy.
Confidential computing means using technology that protects data when it is stored, moving, or being used. TEEs are a big part of this. They make sure data is safe even on cloud servers that might not be trusted.
Michal Wachstock says confidential computing is very important for healthcare, where data sharing and AI use are growing but privacy rules must be followed. It works well on local servers, cloud, or edge networks, fitting different IT setups in medical offices.
Healthcare providers can think about confidential computing by looking at:
Using strong encryption methods like AES-256 and ML-KEM-1024 key exchange, confidential computing also protects healthcare data against future threats from quantum computers, keeping data safe for a long time.
Medical offices often have trouble handling patient calls. AI can help by automating tasks like appointment booking, answering common questions, and directing calls correctly. It is important to use secure AI systems to keep patient data safe during these calls.
Companies like Simbo AI use AI and privacy tools such as federated learning, homomorphic encryption, and TEEs to provide secure phone automation. Their solutions:
Simbo AI helps healthcare groups improve communication processes while keeping data confidential.
Health administrators, owners, and IT leaders in the U.S. need careful planning to use AI with homomorphic encryption and TEEs.
Important areas to consider are:
AI is improving front-office work in medical offices by using privacy tools. Simbo AI’s phone automation is an example of how secure AI helps patient communication while protecting privacy.
Using AI for calls under privacy rules lets staff spend more time with patients. Training AI models across health systems improves them without risking patient data exposure. Homomorphic encryption and TEEs keep patient information safe during calls.
This kind of automation works well where there is a lot of paperwork and patient needs. AI can handle appointment reminders, insurance checks, and basic health questions while protecting sensitive data at every step.
The mix of Homomorphic Encryption, Trusted Execution Environments, and federated learning is changing how U.S. healthcare teams use AI securely. These tools help follow strict privacy laws and open new ways to use data and automation in clinics and offices. Health leaders can guide their teams to safely adopt AI that improves care without risking patient privacy or security.
Agentic AI is an advanced form of artificial intelligence that combines large language models, retrieval-augmented generation, and structured decision-making to create autonomous, goal-driven systems capable of real-time interaction and adaptation with minimal human oversight.
Agentic AI requires large, high-quality, and diverse datasets to learn, adapt, and optimize decision-making processes effectively. More data allows it to tailor its responses in real time based on evolving conditions.
The primary challenge for Agentic AI in healthcare is the inability to access and use valuable patient data due to strict privacy laws and compliance regulations, such as HIPAA, which restrict data sharing.
Privacy-Enhancing Technologies (PETs) are advanced methods that enable AI systems to analyze and learn from sensitive data without exposing raw information, thus addressing privacy concerns while promoting AI innovation.
Federated Learning allows multiple devices or institutions to train AI models collaboratively without transferring sensitive data to a central server. This enables learning from a diverse set of data while preserving privacy.
Differential Privacy protects individual identities within datasets by adding statistical noise, making it impossible for AI to identify specific records, thereby allowing analysis of sensitive information without compromising privacy.
Homomorphic Encryption allows AI models to perform computations on encrypted data without needing to decrypt it, ensuring that sensitive information remains confidential during analysis.
TEEs provide a secure, isolated environment for processing sensitive data, ensuring that AI computations are tamper-proof and can be conducted without exposing any confidential information.
Real-world applications of PETs include financial data sharing among Central Banks using CBDCs to secure transactions and multi-party analytics in healthcare using TEEs to enable collaborative AI analysis without privacy violations.
Duality Technologies offers a platform that integrates PETs, allowing organizations to leverage disparate datasets securely while ensuring compliance with privacy regulations, ultimately driving AI innovation across sectors like healthcare and finance.
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