Background: The advent of digital technology has significantly altered ways of writing. While typing has become the dominant mode of written communication, handwriting remains a fundamental human skill, and its profound impact on cognitive processes continues to be a topic of intense scientific scrutiny. Methods: This paper investigates the neural mechanisms underlying handwriting and typing, exploring the distinct cognitive and neurological benefits associated with each. By synthesizing findings from neuroimaging studies, we explore how handwriting and typing differentially activate brain regions associated with motor control, sensory perception, and higher-order cognitive functions. Results: Handwriting activates a broader network of brain regions involved in motor, sensory, and cognitive processing. Typing engages fewer neural circuits, resulting in more passive cognitive engagement. Despite the advantages of typing in terms of speed and convenience, handwriting remains an important tool for learning and memory retention, particularly in educational contexts. Conclusions: This review contributes to the ongoing debate about the role of technology in education and cognitive development. By understanding the neural differences between handwriting and typing, we can gain insights into optimal learning strategies and potential cognitive advantages, in order to optimize educational, cognitive, and psychological methodologies.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder that impairs motor function, including the fine motor control required for handwriting. Traditional diagnostic methods often lack sensitivity and objectivity in the early stages, limiting opportunities for timely intervention. There is a growing need for non-invasive, accessible tools capable of capturing subtle motor changes that precede overt clinical symptoms. Among early PD manifestations, handwriting impairments such as micrographia have shown potential as digital biomarkers. However, conventional handwriting analysis remains subjective and limited in scope. Recent advances in artificial intelligence (AI) and machine learning (ML) enable automated analysis of handwriting dynamics, such as pressure, velocity, and fluency, collected via digital tablets and smartpens. These tools support the detection of early-stage PD, monitoring of disease progression, and assessment of therapeutic response. This paper highlights how AI-enhanced handwriting analysis provides a scalable, non-invasive method to support diagnosis, enable remote symptom tracking, and personalize treatment strategies in PD. This approach integrates clinical neurology with computer science and rehabilitation, offering practical applications in telemedicine, digital health, and personalized medicine. By capturing dynamic features often missed by traditional assessments, AI-based handwriting analysis contributes to a paradigm shift in the early detection and long-term management of PD, with broad relevance across neurology, digital diagnostics, and public health innovation.