AIDAN is a novel multimodal recurrent neural network (RNN)-based system designed for analyzing EEG and ECG data, with applications in clinical diagnostics and human-robot interaction (HRI). This intelligent system leverages deep learning techniques to enhance the accuracy of biomedical signal processing, aiming to improve patient monitoring and diagnosis. AIDAN uses a 3D Long Short-Term Memory (LSTM) network to extract temporal dependencies from EEG and ECG signals, offering better performance compared to traditional methods in detecting physiological abnormalities. The system processes these signals to identify stress and emotional states, with potential for integration into robotic systems, such as sensor-equipped “Robo-patients” and AI-driven diagnostic tools. AIDAN’s ability to simulate physiological responses can be used to create realistic training environments for medical professionals and enhance diagnostic accuracy, particularly in areas like breast pathology. A wearable device further supports real-time monitoring by continuously transmitting patient data to the system, triggering responses from robots when stress or other health signals are detected. Additionally, AIDAN includes a feedback loop that refines its predictions over time, adapting to the patient's needs and improving system performance. This integration of biosignal processing into HRI is particularly promising for creating more empathetic and responsive robotic healthcare systems. While AIDAN is in its developmental stages, its potential to transform patient care by providing personalized support and real-time interventions marks a significant advancement in medical robotics and healthcare technology.