Researchers at the University of Sydney have developed an ultra-compact AI chip using light for rapid and energy-efficient image computation, successfully classifying medical images including MRI scans.
Key Details
- 1Nano photonic AI chip performs neural computations using light, not electricity.
- 2Prototype was validated by classifying over 10,000 biomedical images, including MRI scans of breast, chest, and abdomen.
- 3Achieved classification accuracy of approximately 90-99% in experiments and simulations.
- 4Chip performs calculations on a picosecond timescale (trillionths of a second).
- 5The technology aims to enable faster, more energy-efficient AI processing with a minimal energy footprint.
- 6A patent has been filed and further work is planned to scale the technology.
Why It Matters
This innovation in AI hardware could drastically reduce power consumption and increase the speed of medical imaging AI, offering a sustainable solution as computational demands grow in clinical radiology. It may enable real-time analysis and more scalable deployment of imaging AI systems.
Source
EurekAlert
Related News
•EurekAlert
AI Accelerates Radiopharmaceuticals, Boosts Personalized Dosimetry in Cancer
Machine learning is driving advancements in radiopharmaceutical drug discovery and optimizing patient-specific dosimetry for precision cancer therapy.
•EurekAlert
Physicians Overly Trust Erroneous AI, Ignore Contradictory Evidence
Physicians tend to trust incorrect AI advice, even when evidence contradicts it, suggesting risks in clinical decision-making with AI tools.
•EurekAlert
Concerns Raised Over Unverified Datasets in AI Health Prediction Models
A new study finds widely used AI health prediction models are built on datasets with unverifiable origins, raising safety and validity concerns.