Endometriosis often takes years to diagnose. This ultrasound simulation innovation could help change that
Updated
March 17, 2026 1:01 AM
A group of women facing backwards. PHOTO: UNSPLASH
Endometriosis affects roughly one in ten women worldwide, yet diagnosing the condition often takes years. In many cases, patients experience symptoms for nearly a decade before receiving a confirmed diagnosis. One reason is that detecting endometriosis through ultrasound requires specialized training and clinicians do not always encounter enough real cases to build that expertise.
To address this gap, medical simulation company Surgical Science has introduced a new ultrasound training module designed specifically for identifying endometriosis. The system allows clinicians to practice scanning techniques in a virtual environment, helping them recognize signs of the disease without relying solely on real-patient cases.
A key feature of the simulator is training on the “sliding sign,” an ultrasound indicator used to detect deep endometriosis. Because the condition can appear differently from patient to patient, mastering this assessment in real clinical settings can be difficult. The simulator allows clinicians to repeat the process across multiple scenarios, improving their ability to identify the condition during routine examinations.
The module also incorporates the International Deep Endometriosis Analysis (IDEA) protocol, which provides a structured method for performing a complete pelvic ultrasound assessment. Additional training cases, region-based scenarios and certification options are included to support standardized learning.
Early training results suggest strong improvements in clinician confidence, including higher skill levels in transvaginal ultrasound and better recognition of deep endometriosis. By expanding access to structured ultrasound training, simulation tools like this could help reduce diagnostic delays and improve care for millions of women living with the condition.
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A step forward that could influence how smart contracts are designed and verified.
Updated
January 8, 2026 6:32 PM
ChainGPT's robot mascot. IMAGE: CHAINGPT
A new collaboration between ChainGPT, an AI company specialising in blockchain development tools and Secret Network, a privacy-focused blockchain platform, is redefining how developers can safely build smart contracts with artificial intelligence. Together, they’ve achieved a major industry first: an AI model trained exclusively to write and audit Solidity code is now running inside a Trusted Execution Environment (TEE). For the blockchain ecosystem, this marks a turning point in how AI, privacy and on-chain development can work together.
For years, smart-contract developers have faced a trade-off. AI assistants could speed up coding and security reviews, but only if developers uploaded their most sensitive source code to external servers. That meant exposing intellectual property, confidential logic and even potential vulnerabilities. In an industry where trust is everything, this risk held many teams back from using AI at all.
ChainGPT’s Solidity-LLM aims to solve that problem. It is a specialised large language model trained on over 650,000 curated Solidity contracts, giving it a deep understanding of how real smart contracts are structured, optimised and secured. And now, by running inside SecretVM, the Confidential Virtual Machine that powers Secret Network’s encrypted compute layer, the model can assist developers without ever revealing their code to outside parties.
“Confidential computing is no longer an abstract concept,” said Luke Bowman, COO of the Secret Network Foundation. “We've shown that you can run a complex AI model, purpose-built for Solidity, inside a fully encrypted environment and that every inference can be verified on-chain. This is a real milestone for both privacy and decentralised infrastructure”.
SecretVM makes this workflow possible by using hardware-backed encryption to protect all data while computations take place. Developers don’t interact with the underlying hardware or cryptography. Instead, they simply work inside a private, sealed environment where their code stays invisible to everyone except them—even node operators. For the first time, developers can generate, test and analyse smart contracts with AI while keeping every detail confidential.
This shift opens new possibilities for the broader blockchain community. Developers gain a private coding partner that can streamline contract logic or catch vulnerabilities without risking leaks. Auditors can rely on AI-assisted analysis while keeping sensitive audit material protected. Enterprises working in finance, healthcare or governance finally have a path to adopt AI-driven blockchain automation without raising compliance concerns. Even decentralised organisations can run smart-contract agents that make decisions privately, without exposing internal logic on a public chain.
The system also supports secure model training and fine-tuning on encrypted datasets. This enables collaborative AI development without forcing anyone to share raw data—a meaningful step toward decentralised and privacy-preserving AI at scale.
By combining specialised AI with confidential computing, ChainGPT and Secret Network are shifting the trust model of on-chain development. Instead of relying on centralised cloud AI services, developers now have a verifiable, encrypted environment where they keep full control of their code, their data and their workflow. It’s a practical solution to one of blockchain’s biggest challenges: using powerful AI tools without sacrificing privacy.
As the technology evolves, the roadmap includes confidential model fine-tuning, multi-agent AI systems and cross-chain use cases. But the core advancement is already clear: developers now have a way to use AI for smart contract development that is fast, private and verifiable—without compromising the security standards that decentralised systems rely on.