Digital Healthcare in Future Medicine: From Research to Clinical Practice in Neurology

Digital healthcare involves applying technology-driven and data-oriented approaches to enhance the health of both individual patients and the population as a whole, and offers continuous monitoring and extends access to care beyond traditional clinical settings. This expansive domain includes telemedicine, remote patient monitoring, mobile healthcare applications, and advanced data analytics, which collectively aim at improving accessibility, reducing costs, and personalizing medical care. The integration of artificial intelligence in digital healthcare is revolutionizing decision-making in clinical practice, optimizing data for utilization in support systems, and empowering patients to actively manage their health.

The COVID-19 pandemic served as a catalyst for the adoption of digital healthcare technologies and underscored their critical role in providing remote care and improving health outcomes. With a projected compound annual growth rate of around 15% from 2021 to 2028, the digital healthcare market has significant momentum, which reflects active research and application developments within clinical settings.¹

Digital healthcare has become a focal point for research in the realm of clinical neurology, particularly in software-driven interventions aimed at diagnosing, managing, and treating neurological disorders more effectively.² The Journal of Clinical Neurology has reported on notable studies that exemplify the transformative impact of digital healthcare. For example, a machine-learning algorithm utilizing heart-rate data demonstrated significant potential in screening for orthostatic hypotension, which emphasized the preventive capabilities of digital tools.³ Another study employed recurrent neural networks to predict the progression from mild cognitive impairment to Alzheimer’s dementia based on the findings of neuropsychological test series, which illustrated how predictive models can improve prognoses and enable tailored patient management.⁴

Furthermore, deep-learning algorithms have shown promise in detecting Parkinson’s disease (PD) through the analysis of medical claims data. An LSTM-based model achieved a remarkable around 94% accuracy using diagnostic codes over a 4-year-period preceding a PD diagnosis, showcasing the usefulness of longitudinal data in early detection.⁵ Another approach involved integrating diagnostic and medication data to increase the predictive accuracy during earlier prodromal stages, which highlighted the value of comprehensive healthcare data in achieving personalized interventions.⁶

Beyond diagnostics, therapeutic applications of digital healthcare are also advancing. A recent study introduced a customized visual discrimination therapy for chronic stroke patients with visual field defects, which highlighted the potential of tailored digital therapeutics in improving rehabilitation outcomes.⁷ Similarly, machine-learning methods have been utilized to predict all-cause mortality in patients with obstructive sleep apnea using sleep-related data from wearable devices, which represents an advancement in the use of preventive analytics in sleep medicine.⁸

A review article introduced emerging technologies such as functional near-infrared spectroscopy that are further expanding digital healthcare by enabling real-time brain monitoring using portable devices.⁹ Recent research underscores two pivotal trends: 1) multimodal monitoring to assess cerebral and systemic physiological interactions, and 2) naturalistic experimental paradigms in dynamic and immersive studies, including virtual reality environments. These developments demonstrate the potential of new technologies in personalizing neurological assessments, optimizing therapy protocols, and facilitating digital innovation.

Despite its promise, digital healthcare faces challenges that need to be addressed. Validating these technologies in diverse populations is essential to ensure their generalizability and efficacy, while ethical considerations including data privacy and security as well as informed patient consent must remain central to development efforts. Furthermore, addressing disparities in digital literacy and integrating these tools seamlessly into existing healthcare systems require strategic planning, adequate resources, and comprehensive training for healthcare professionals.

In conclusion, digital healthcare offers unprecedented opportunities to revolutionize traditional medical practices—including in clinical neurology—by delivering personalized, efficient, and proactive care. However, fully integrating digital healthcare into clinical practice will require rigorous research, ethical oversight, and thoughtful implementation to achieve its potential in improving patient care and health outcomes.

Availability of Data and Material

Data sharing not applicable to this article as no datasets were generated or analyzed during the study.