| Title | Adaptive Deep Learning Architectures for Real-Time Healthcare Diagnostics |
| Research Area | Deep Learning |
| Abstract | Advances in artificial intelligence have transformed healthcare diagnostics, yet traditional deep learning architectures often struggle to adapt to rapidly changing patient data streams. This paper proposes an adaptive deep learning framework integrating reinforcement learning with convolutional neural networks (CNNs) to enhance diagnostic accuracy in real time. The system dynamically adjusts its parameters and feature extraction layers based on continuous feedback from incoming data, enabling faster and more precise predictions. Experimental simulations on benchmark medical datasets demonstrate a significant improvement in classification accuracy and latency reduction compared to static models. The findings underscore the potential of adaptive AI systems to revolutionize healthcare diagnostics, particularly in emergency and point-of-care settings. |
| Keywords | Deep Learning, Healthcare AI, Reinforcement Learning, Real-Time Diagnostics. |
| Paper Status | Published |
| Volume | 1 |
| Issue | 1 |
| Published On | 08/02/2025 |
| Published File |
IJSRTD_4840.pdf
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