Background: Parkinson's disease (PD) is heterogeneous, both clinically, genetically and neuropathologically, which presents ongoing challenges for conventional syndromic clinical diagnostic criteria and uniform treatments. Recent advances in biofluid biomarkers, high-resolution neuroimaging and artificial intelligence (AI) have provided frameworks to reclassify PD on an objective biological basis and personalize therapeutic interventions 4 .
Objective: To critically synthesize recent advances in AI-driven diagnostic modalities, biological disease staging frameworks, biofluid biomarkers, genotype-targeted therapeutics and adaptive clinical trial designs in PD7.
Data Sources: A search was conducted in PubMed, MEDLINE, Scopus, Web of Science and other major biomedical literature databases for studies published mostly between 2018 and 20244.
Review Methods: Evidence from clinical trials, neuroimaging studies, digital sensor evaluations, biofluid biomarker investigations, and regulatory science guidelines was reviewed using a comparative synthesis approach2 .
Key Findings: The biological definition of PD, formalized by the Neuronal Alpha-Synuclein Disease Integrated Staging System (NSD-ISS), bases disease classification on in vivo seeded aggregation of alpha-synuclein () and dopaminergic neurodegeneration ()4. Deep learning models applied to single-photon emission computed tomography (SPECT) and magnetic resonance imaging (MRI) yield diagnostic classification accuracies in the range of 88%–99%5. Non-invasive digital devices such as radio-wave nocturnal breathing sensors and video-based kinetic software allow continuous surveillance at home6. Biomarker-enriched clinical trials8 are being driven by targeted therapeutics, including small-molecule and antisense oligonucleotide agents targeting LRRK2 and GBA1 mutations. But the implementation of these algorithms in practice is still held back by algorithmic overfitting, a lack of validation in diverse non-European cohorts and regulatory concerns around continuous machine learning updates13.
Conclusion: The combination of biological staging and AI-enabled digital phenotyping provides a scalable paradigm for early detection and targeted therapeutic selection in PD4. Standardized analytical pipelines, harmonized regulatory oversight, and adaptive clinical trial designs10 are needed for clinical translation.
Keywords: Parkinson Disease, Artificial Intelligence, Precision Medicine, Biomarkers, Machine Learning, Neurodegenerative Diseases