Fetch.ai (@Fetch_ai): Confidential Medical AI Agents: Fetch.ai × Secret Network

Confidential Medical AI Agents: Fetch.ai × Secret Network Every 14 seconds, a woman somewhere in the world receives a mammogram, a procedure that can change the course of her life. Early detection remains the most powerful tool in the fight against breast cancer, yet despite advances in AI-assisted imaging, adoption has lagged behind potential. Privacy, trust, and regulation continue to limit where and how data can be used. Fetch.ai and @SecretNetwork are changing that. The collaboration between Fetch.ai and Secret Network is entering a new and more impactful chapter. What began as a joint exploration into privacy-preserving artificial intelligence has matured into a practical framework for processing sensitive healthcare data securely, transparently, and with unwavering respect for patient privacy. Both networks are built on the Cosmos SDK, a foundation designed for interoperability and scalability. This shared technical lineage, combined with a mutual conviction that selective disclosure and verifiable computation are essential for deploying AI in high-stakes environments, continues to drive and deepen our partnership. This new phase focuses on medical imaging, one of the most data-intensive and regulated areas of healthcare. Together, Fetch.ai and Secret Network have developed a confidential AI pipeline that analyzes mammography scans (DICOM images) inside trusted hardware environments using Density, Mirai, AsymMirai, and Medical LLM. The system produces structured diagnostic reports without ever exposing raw patient data to untrusted infrastructure. It is a tangible example of what confidential AI looks like in practice, not just in theory. The live AI Agent is accessible at: https://agentverse.ai/agents/details/agent1q2x99m268yjfh5u5t3xse25gsevt5aqka8w6hvnh7m36tz655muskkj8rzx/profile for everyone to explore. Why Existing AI Models Struggle in Practice Radiology sits at the heart of modern diagnostics. AI has already demonstrated its ability to support radiologists. Models such as CheXNet, Lunit, Aidoc, and Qure.ai have matched or exceeded human accuracy in controlled studies. Yet, in real hospital environments, these systems face limits. Differences in imaging protocols, equipment calibration, and patient demographics often cause models to lose reliability once deployed outside their training datasets. Many struggle to generalize across institutions or handle rare and subtle cases. Still, several innovative companies such as Civie.com have demonstrated that AI can find strong product-market fit when applied responsibly, delivering measurable improvements to healthcare workflows and diagnostics. These efforts show the potential of AI in clinical environments, but they also reveal the limits of today’s infrastructure. As AI adoption grows, the need for secure, interoperable systems becomes increasingly clear. The shared infrastructure being built by Fetch.ai and Secret Network can serve as a trusted substrate for companies like Civie and others, providing privacy, verifiability, and scalability as standard features rather than afterthoughts. The gap between benchmark and bedside performance has proven stubborn. Regulations also slow adoption, as most systems can only assist rather than act autonomously, and legal frameworks have yet to accommodate full algorithmic responsibility. Above all, trust remains fragile: hospitals and patients alike must be certain that private data never leaves secure hands. The Fetch.ai and Secret Network solution was designed to close these gaps. By combining confidential computing with decentralized AI agents, it allows medical data to be analyzed privately and verifiably, ensuring accuracy and confidentiality coexist. In this model, privacy isn’t an afterthought—it’s part of the computation itself. A Collaboration Built on Complementary Strengths Both Fetch.ai and Secret Network bring distinct technologies to the table, united by a shared goal: intelligent systems should never require users to surrender their data. Fetch.ai contributes its agent-based uAgents framework and agent discoverability layer, known as the Agentverse. This infrastructure allows autonomous agents; digital entities that can communicate, reason, and act to interact across decentralized environments. Within healthcare, these agents can serve as secure intermediaries between systems, exchanging data or requesting services without revealing underlying information and acting as diagnostic support tools. Secret Network provides the confidential computing foundation through its SecretVM architecture, built on Intel TDX and NVIDIA Confidential Computing. This infrastructure allows code to run inside trusted execution environments (TEEs), isolated from the host system and invisible to anyone outside it. Even if the hardware owner or cloud provider is compromised, the data inside the enclave remains protected. The two frameworks complement each other. Fetch.ai’s interoperable agent ecosystem and Secret Network’s confidential computing layer form a continuous chain of trust. Data enters a TEE, is processed by AI models inside a confidential virtual machine, and leaves only as encrypted or structured output. The entire process can be verified cryptographically, ensuring integrity without sacrificing privacy. Privacy by Design, Compliance by Architecture For AI in healthcare, compliance is not negotiable. The Fetch.ai and Secret Network architecture is designed to meet the world’s most stringent privacy requirements from the ground up. By keeping all computation within TEEs and enforcing hardware-level encryption, the system aligns naturally with the standards set by HIPAA, GDPR, the FDA’s AI/ML guidance, and the EU AI Act. Each layer of the confidential computing stack is measured and validated before it runs, forming what engineers call a “chain of trust.” Every component from firmware to operating system to application is verified before control is passed on. Compliance, in this sense, becomes a technical property rather than a policy aspiration. Hospitals, researchers, and patients can have full confidence that data remains confidential and that AI-driven insights are both accurate and auditable. Inside the Confidential AI Pipeline The architecture operates through three main stages. First, secure ingestion. Medical images are uploaded through a confidential web interface hosted in SecretVM. From the moment an image enters the system, it is encrypted and isolated inside trusted hardware. Second, confidential inference. Within the enclave, two AI models, Mirai and AsymMirai, run in parallel. The first, Mirai is a longitudinal risk prediction model trained to estimate the probability of developing breast cancer within 1–5 years, using a patient’s four-view screening mammogram and non-image data (if available: age, density, prior exams) The second, AsymMirai, extends Mirai by modeling bilateral dissimilarity between the left and right breasts and generating interpretable overlays that visually highlight where asymmetries drive risk. The third, Llama3-Med42-70B, converts those findings into a structured diagnostic report written in clinical language. All models run entirely inside the protected environment, which combines Intel TDX isolation with NVIDIA Confidential GPUs for high-performance computation. Finally, secure delivery. The resulting report is passed to Fetch.ai’s Agentverse, where a specialized medical agent handles communication with authorized recipients. This agent can interact with other agents such as those managing patient records, telemedicine services, or insurance assessments without leaking any private data. The outcome is a verifiable, end-to-end workflow that strengthens clinicians rather than replaces them. As radiology has proven over the years, technology does not eliminate the need for human expertise, it amplifies it. Confidential AI preserves this balance by making AI a partner, not a substitute. Building Trust Through Attestation Trust in AI cannot depend on reputation or marketing. It must be proven mathematically. SecretVM integrates a hardware-level attestation process that produces a signed “attestation quote” for every computation. This cryptographic proof verifies that the workload was executed inside a genuine, uncompromised TEE and that the expected code and models were used. For developers, attestation proves that their algorithms run securely without exposing proprietary details. For hospitals and regulators, it creates an auditable record of execution integrity. For patients, it delivers something long missing from AI healthcare systems: confidence that their personal data remains theirs. A Timely Advancement and Shared Impact As this work moves into production, its timing carries special meaning. The rollout coincides with October’s global Breast Cancer Awareness Month, a period dedicated to progress in early detection, treatment, and education. For the Fetch.ai and Secret Network teams, this alignment is a reflection of shared purpose as much as timing. Mammography remains one of the most critical tools in early detection, and bringing confidentiality and verifiable trust to that process echoes the broader spirit of awareness month: empowering better outcomes through knowledge, technology, and care. This collaboration benefits both networks in profound ways. For Fetch.ai, it demonstrates how autonomous agents can power real-world applications in sectors where privacy and compliance are paramount. The integration opens the door for a new generation of healthcare agents that can operate securely, coordinate across institutions, and contribute to better clinical outcomes without breaching privacy laws. For Secret Network, it validates the scalability and robustness of its confidential computing infrastructure under demanding, GPU-intensive workloads. Healthcare is among the most challenging environments to serve, and success here signals readiness for deployment across other regulated domains such as finance, law, and defense. Together, Fetch.ai and Secret Network are creating a model for how Web3 technologies can deliver real public value, combining decentralization, intelligence, and privacy into a framework that serves both human and institutional trust. This confidential AI pipeline strengthens clinicians while protecting the dignity and privacy of every patient whose data is part of the story. Behind every scan is a life, a family, and a future worth protecting. A Shared Commitment to Responsible Intelligence The Fetch.ai and Secret Network partnership has always been about more than technical compatibility. It represents a shared belief that artificial intelligence must respect the boundaries of privacy and accountability that define human trust. In healthcare, those boundaries are sacred. By showing that AI systems can be both powerful and private, the two networks are helping reshape how institutions think about data. They are proving that the path to responsible AI does not lie in restricting innovation but in building it on the right foundations. This is the future Fetch.ai and Secret Network are working toward: a healthcare system rooted in transparency, accountability, and hope. A reminder that progress in AI is most meaningful when it serves the people behind the data. We invite everyone to explore the live AI Agent on the @Agentverse_ai at: https://agentverse.ai/agents/details/agent1q2x99m268yjfh5u5t3xse25gsevt5aqka8w6hvnh7m36tz655muskkj8rzx/profile