The Silicon Pulse: Can AI Decode Human Emotions Better Than The Clinical Eye? A Systematic Review

Abstract

Background: Artificial intelligence (AI) has rapidly emerged as a transformative tool in decoding human emotions, offering unprecedented potential to augment clinical diagnostics and psychological assessment.

Objective: This systematic review aims to evaluate the extent to which AI systems can recognize and interpret human emotions with accuracy comparable to, or exceeding, human clinical judgment across psychiatric and neurological contexts.

Methods: Following PRISMA 2020 guidelines, ten empirical studies published between 2016 and 2025 were synthesized from databases including PubMed, IEEE Xplore, Scopus, and JMIR. Studies examining AI-driven emotion recognition through facial, vocal, linguistic, or multimodal data were included. Quality was assessed using the Newcastle–Ottawa Scale and Cochrane RoB 2 tool.

Results: AI-based systems demonstrated accuracy rates ranging from 77% to 99.8%, frequently surpassing human raters in structured emotion tasks. Deep learning models, such as convolutional neural networks (CNNs) and transformer architectures, achieved superior sensitivity and specificity in detecting depressive, autistic, and neurological emotional markers. Speech emotion recognition and EEG-based models further complemented multimodal detection with measurable correlations to psychiatric scales.

However, empathy perception and contextual interpretation remained limitations compared to expert clinicians.

Conclusions: AI demonstrates robust diagnostic and affective recognition capabilities, often rivalling human assessment in precision and scalability. Nevertheless, integration into clinical contexts must prioritize ethical oversight, interpretability, and emotional authenticity to ensure human–AI complementarity rather than replacement.