AI in healthcare Insights: 20th November - 26th November' 2025

 

Gel-free 3D-printed electrodes + AI detect arrhythmias in wearables

Researchers at Simon Fraser University have developed a heart monitoring system combining 3D printing and AI to improve cardiac diagnostics. The system uses reusable, dry 3D-printed electrodes with an origami-inspired design that adheres to the skin without sticky gel, addressing issues of patient discomfort and signal degradation over time. 

The embedded AI software is capable of pre-diagnosing up to 10 types of arrhythmias in real-time. Clinical testing indicates the design improves patient compliance compared to traditional Holter monitors. The system is designed to allow test results to be shared electronically with physicians for confirmation, aiming to expand access to monitoring in remote and underserved communities.

Read the original article at https://www.sfu.ca/sfunews/media/media-releases/2025/11/ai-at-the-heart-of-new-sfu-gel-free-ecg-system-for-faster-diagno.html

Our Opinion: This shift solves long-standing issues of patient discomfort, compliance, and significant medical waste associated with traditional ECG patches. This technology will not only accelerate the time to diagnosis for critical conditions like atrial fibrillation but also provide easy access to specialized heart health management, making super-accurate, long-term patient monitoring possible even outside the hospital. It’s a perfect illustration of how smart engineering can maximize the clinical utility of AI.

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AI outperforms radiologists in spotting invisible airway foreign bodies

A study from the University of Southampton tested an AI tool designed to detect radiolucent foreign body aspiration (FBA) on CT scans, objects like food fragments that are faint or invisible to the human eye. In a test involving 70 CT scans, the AI model detected 71% of the confirmed FBA cases, significantly higher than the 36% detected by expert radiologists. However, the AI showed lower precision (77%) compared to the radiologists (100%), meaning it generated some false positives. The model uses a deep learning neural network combined with airway mapping techniques to flag anomalies that human reviewers typically miss.

 The findings underscore the model's potential to dramatically improve diagnostic reliability and speed in complex, subtle, and potentially fatal clinical situations.

Read the original article at https://www.outlookindia.com/healthcare-spotlight/researchers-develop-ai-tool-that-outperforms-radiologists-in-detecting-hidden-airway-objects

Our Opinion: While the AI's high sensitivity in detecting elusive foreign bodies is impressive, catching 71% of cases vs 36% is impressive, a 77% precision rate means nearly 1 in 4 alerts is a false positive. In a busy clinical setting, this high rate of false alarms could lead to alarm fatigue, unnecessary follow-up procedures, or redundant human review, potentially slowing down treatment rather than accelerating it. The true clinical value lies not just in high sensitivity but in a reliable balance between sensitivity and precision. Although a powerful innovation, there is a lot of scope for this AI to act as a reliable assistant.

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Military health optimisation uses wearables, AI & personalised wellness programmes

The Department of Defense (DoD) is implementing the Optimizing the Human Weapon System (OHWS) program, which distributes commercial wearables (like Oura rings and Garmin watches) to service members. The program aggregates physiological data—such as sleep, heart rate variability, and activity—to offer personalized wellness programs and injury prevention strategies. Data is shared with medical officers and fitness staff to monitor unit readiness. 

The initiative aims to shift military health from a reactive model to a proactive "performance continuum," using data analytics to inform training and deployment decisions.

Read the original article at https://www.aerospacedefensereview.com/news/military-health-optimization-a-path-to-enhanced-readiness-nwid-2411.html

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AI model detects hidden chest scan objects better than experts

A new clinical study demonstrates that AI can detect potentially malignant pulmonary nodules on routine chest X-rays (CXRs), even when the scans were ordered for non-respiratory reasons. Acting as a "second reader," the AI flagged suspicious nodules in patients with no prior cancer suspicion, leading to earlier surgical intervention. 

The study suggests that AI can effectively repurpose low-cost, routine imaging into an opportunistic screening tool for lung cancer, potentially identifying high-risk pathologies that would otherwise go unnoticed until later stages.

Read the original article at https://medicalxpress.com/news/2025-11-ai-hidden-chest-scans-radiologists.html

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