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The convergence of artificial intelligence (AI) and wearable biosensors is revolutionizing personalized healthcare, enabling continuous monitoring, early detection of health issues, which enhances the efficiency of data processing and real-time decision-making. Multimodal Large Language Models (MLLMs) play a pivotal role in this ecosystem by offering advanced capabilities in analyzing complex health data, understanding nuanced health contexts, and generating tailored health recommendations instantaneously. This study provides insights into how machine learning, deep learning algorithms, and MLLM can work together to facilitate the analysis of physiologic data for real-time monitoring and early warning systems as well as complex decision support mechanisms.
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Dynamic changes in dopamine, noradrenaline, and serotonin release are believed to causally contribute to the neural computations that support reward-based decision making. Accordingly, changes in signaling by these systems are hypothesized to underwrite multiple cognitive and behavioral symptoms observed in many neurological disorders. Here, we characterize the release of these neurotransmitters measured concurrently in the caudate of patients with Parkinson's disease or essential tremor undergoing deep brain stimulation surgery as they played a social exchange game.
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