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Building a telehealth platform at clinical scale means solving for hospital network restrictions, HIPAA compliance and auditability, and the data load of continuous remote monitoring – and the architecture decisions that determine whether it holds up are mostly made in the first few sprints. The engineering debt from early decisions starts showing up at scale: video sessions dropping when hospital firewalls, restrictive egress policies, or network address translation prevent a direct media path;

A radiologist on a standard hospital shift may read dozens to well over a hundred imaging studies, depending on subspecialty, setting, shift structure, and case complexity. Each one is a search for something that might be subtle, easy to miss, or buried in noise. At that volume, non-trivial discrepancy or error rate is a known risk in radiology practice, especially under high workload and time pressure. Radiologists are working through growing imaging volumes with a workforce that has never full

In 2018, a clinical informaticist launched a tool to handle intake forms and clinical notes so doctors could spend less time typing and more time doctoring. A small study with 18 medical students suggested that the Cydoc smart intake form could substantially reduce note-writing time while maintaining note quality, although broader validation in practicing clinicians was still needed. By August 2025, the company was gone. The postmortem names the main reason: Cydoc lived outside the EHR. Doctors

Chatbots helped businesses get started with AI, but their impact has been limited — they respond to questions, follow scripts, and stop at the conversation. They don’t take action. AI agents do. These systems can plan, decide, and carry out tasks across tools like CRMs, ERPs, and internal platforms — all with minimal human input. They act more like digital team members than assistants. Gartner projects that by 2026, 40% of enterprise applications will include task-specific AI agents, up from und

AI-powered products can create real value, but only when they continue working reliably in the hands of customers. What makes this difficult is that their behavior doesn’t stay fixed after release. As data changes, so does model performance, which means that quality can decline even when no one touches the code. According to the 2024 DORA report, elite teams typically deploy on demand (multiple times per day), recover from failed deployments in under an hour, and keep change failure rates around

A computer vision model can look perfect during testing and then fall apart the moment it meets real life. The contrast is often dramatic. An MIT review found some face-analysis systems making mistakes on 34.7% of dark-skinned women, while the error rate for light-skinned men stayed under 1%. In agriculture, models that scored 95–99% accuracy on clean lab photos fell to 70–85% on real crops. And in radiology, an RSNA review showed four out of five models performing worse on data from another hos

Voice is becoming the new interface of work. From warehouses to call centers, AI-powered assistants are showing up everywhere: faster, hands-free, always on. The scale is staggering: the Voice AI Agents Market is forecasted to grow from $2.4 billion to $47.5 billion by 2034, and the approximate number of voice assistants in use globally is now over 8 billion – more than people on the planet. But recognizing speech isn’t enough anymore. In high-stakes environments like healthcare, voice AI ne

AI is transforming cancer care in incredible ways, and understanding how its success is measured is key to assessing its impact. In this article, we’ll start with a quick, straightforward explanation of key AI metrics like AUC and Dice scores. Once that foundation is set, we’ll explore real-world AI in cancer research showing is advancing early cancer detection with machine learning models, improving diagnoses, personalizing treatments, and even speeding up drug discovery. These examples demons