What Counts as Early Detection of Parkinson's Disease? An Evidence Framework for Artificial Intelligence Research

Abstract Artificial intelligence studies in Parkinson's disease often use the phrase early detection for tasks that differ substantially in their clinical meaning. Recognising an established diagnosis, estimating a prodromal research probability, identifying a molecular abnormality, and predicting a future clinical diagnosis require different participants, reference standards, and evaluation methods. This selected narrative review examines those distinctions using original clinical criteria, longitudinal cohort studies, biomarker investigations, and prediction-model methods. Sources were selected through targeted web searches on 16 September 2026; this was not a systematic review or meta-analysis. The analysis separates evidence about observable function from evidence about underlying biology and future clinical events. Digital movement and breathing studies, plasma proteomics, and alpha-synuclein seed amplification provide complementary research opportunities, but their results cannot be transferred directly to unrestricted population screening. We propose a claim-to-design framework organised around the intended population, index date, outcome, prediction horizon, comparator, and action triggered by a result. Particular attention is given to diagnostic uncertainty, participant-level separation, prevalence-dependent predictive value, competing outcomes, and verification bias. A proposed research agenda for NeuralCipher prioritises explicit outcome definitions and independently evaluated referral support before stronger diagnostic claims. The central conclusion is methodological: an early-detection claim becomes interpretable only when the study specifies what is being detected, how early it is measured, and what evidence would establish benefit. 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