Revealing the Complexity of Fatigue: A Review of the Persistent Challenges and Promises of Artificial Intelligence

Abstract

Part I reviews persistent challenges obstructing progress in understanding complex fatigue’s biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades.

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“…Emerging artificial intelligence (AI) methods, including machine learning and deep learning, show promise for consolidating personalized multi-omics data, behavior, and neural signals to advance fatigue research [71]. These techniques could help integrate profiles spanning genetics, molecular markers, self-reports, neuroimaging, and task performance to characterize the heterogeneity in how fatigue manifests across individuals.…”

“…Additionally, machine learning provides approaches to glean insights from complex indicators without relying solely on invasive procedures for measurement. Progress will rely on effective communication and partnerships across disciplines to compile robust consolidated data resources that fully harness these versatile AI capacities for pattern recognition within fatigue's biological complexity [71].…”