A processor can infect calculations if it lacks power or is subjected to inappropriate operating conditions. Here are the main causes and possible consequences in the context of medical devices incorporating AI:
________________________________________
1. POSSIBLE CAUSES OF ERRONEOUS CALCULATIONS
• CPU overload: If the processor is insufficiently sized for the task (e.g., real-time AI calculations), it may skip instructions or generate errors (overflow, underflow, excessive latency).
• Thermal throttling: Overheating can force the CPU to reduce its frequency to protect itself, leading to delays or processing errors.
• Lack of memory (RAM/cache): If data cannot be accessed quickly enough, the processor may produce partial or corrupted results.
• Hardware bugs: A faulty or misconfigured CPU can corrupt calculations (e.g., floating-point errors). ________________________________________
2. Risks to Medical Equipment
• Misdiagnosis: Radiology or laboratory AI could incorrectly analyze images/biomarkers.
• Critical Timelines: In real-time (e.g., cardiac monitoring), latency can render results unusable.
• Algorithmic Bias: Incomplete calculations can distort predictions (e.g., drug dosage).
________________________________________
3. SOLUTIONS TO AVOID THESE PROBLEMS
• Right-Sizing: Use CPUs/GPUs with a power margin (e.g., NVIDIA Jetson for edge AI).
• Software Optimization: Reduce load via lightweight models (quantized, pruned) or dedicated hardware (TPU, FPGA).
• Thermal Control: Active/Passive Cooling to Avoid Throttling.
• Robustness Testing: Validation of calculations under maximum load (benchmarks, extreme scenarios).
• Redundancy: Double-checking by a second system (safety-critical systems).
___________________________________
4. CONCRETE EXAMPLE
An AI system for tumor detection on MRI scans must process high-resolution images. If the CPU/GPU is underpowered:
• Result: False negatives/negatives due to saturation or rounding errors.
• Solution: Use a dedicated GPU (e.g., NVIDIA A100) with optimized libraries (CUDA, TensorRT).
________________________________________
CONCLUSION
In medical equipment, hardware reliability is critical. An underpowered or overloaded processor can compromise the integrity of computations, especially for AI. It is essential to:
• Choose certified hardware (e.g., IEC 62304 standards for medical devices).
• Validate performance in real-world conditions.
• Monitor the hardware (logs, overload alerts).