Psychosocial stress-associated alterations in microRNAs and their relevance for metabolic diseases: A systematic review.

Neurodegenerative disorders are among the leading causes of chronic disability and dependency worldwide, with their impact intensifying as populations age. The urgency of translating artificial intelligence (AI) into clinical practice in this domain arises not only from rapid algorithmic progress but also from the pressing public health need to address diagnostic delays and the escalating socioeconomic burdens. Parkinson’s disease (PD) and Alzheimer’s disease (AD) together account for the majority of progressive motor and cognitive impairment, caregiver strain, and long-term institutional care needs [-]. These conditions impose substantial societal costs through lost productivity, downstream medical expenses, and reliance on informal caregiving. Moreover, inequities in access to specialist assessment and advanced diagnostics further exacerbate late presentation and delayed intervention [,]. A central challenge in neurodegenerative medicine is the long latency between the onset of pathophysiological processes and the emergence of overt symptoms. In PD, this may manifest as nonspecific prodromal features such as hyposmia, constipation, depression, or rapid eye movement (REM) sleep behavior disorder years before motor signs [,]. In AD, it may present as mild cognitive impairment (MCI), a heterogeneous transitional state marked by subtle decline and biomarker abnormalities across extended preclinical phases [,]. This diagnostic delay limits opportunities for early disease-modifying therapies and timely trial enrollment. At the same time, traditional approaches relying on clinical criteria remain vulnerable to inter-rater variability, comorbidities, and atypical or mixed presentations that complicate differentiation because of overlapping nonspecific motor, autonomic, and neuropsychiatric features, as well as atypical parkinsonian disorders and secondary cognitive syndromes such as multiple system atrophy (MSA) and related conditions [-]. Neuroimaging and fluid biomarkers have improved diagnostic confidence. MRI reveals regional patterns of atrophy, positron emission tomography (PET) quantifies amyloid and tau burden, and cerebrospinal fluid (CSF) or plasma assays provide biological specificity. However, their widespread adoption is limited by high cost, invasiveness, assay variability, and reliance on expert interpretation [,,,]. In PD, the absence of a single confirmatory biomarker necessitates reliance on disease progression, treatment response, and supportive imaging, which remain ambiguous in the early stages [,]. Consequently, research has shifted toward automated differentiation systems and digital biomarkers, including gait, speech, and handwriting signals, to capture early dysfunction and improve diagnostic accuracy [].