AI and Neurological Rare & Neuroimmune Diseases ── Parkinson's ── 2026-10-29
Parkinson's disease and parkinsonism: treatment, trials and AI
- Abstract
OBJECTIVES: Advanced Parkinson disease (APD) is often under-recognized, which may delay treatment optimization and referral for device-aided therapy assessment. Machine learning-enabled remote monitoring systems (ML-RMS) may support earlier recognition of progression, but their value depends on whether alerts lead to clinical action and patient benefit. METHODS: We developed a Markov model comparing standard care with ML-RMS-supported care from the Finnish health care perspective. The model simulated persons with Parkinson disease at risk of APD over 16 years. The base case assumed ML-RMS sensitivity and specificity of 88 percent and 62 percent. Costs, quality-adjusted life years (QALYs), incremental cost-effectiveness ratios, and incremental net monetary benefit were estimated. Scenario and probabilistic sensitivity analyses explored diagnostic accuracy, monitoring costs, direct quality-of-life effects, deep brain stimulation (DBS) costs, pathway timing, and delayed nursing home admission. RESULTS: In the base case, ML-RMS generated 0.0044 additional QALYs at an additional cost of €761 per patient, resulting in an incremental cost-effectiveness ratio of €172,023 per QALY gained. At a willingness-to-pay threshold of €35,000 per QALY, the incremental net monetary benefit was negative. Scenario analyses indicated that ML-RMS could become cost-effective if active monitoring produced direct quality-of-life gains or delayed nursing home admission. CONCLUSIONS: ML-RMS is unlikely to be economically favorable if limited to earlier APD recognition and timely DBS treatment in a small eligible subgroup. Its value depends on whether monitoring changes care pathways, improves patient outcomes, or reduces long-term care needs. Future studies should prioritize clinical utility, quality-of-life effects, treatment decisions, and institutional care.
Journal IF-equivalent: 0.7 (OpenAlex 2-year mean citedness, value as of 2026-10-28, retrieved 2026-10-29; not the official Clarivate IF)Reference: Godoy Junior CA, van Deen WK, Mäkitie L, Koivu M, Bakker LJ, Uyl-de Groot CA, et al. Early health technology assessment of machine learning-enabled remote monitoring for advanced Parkinson disease. Int J Technol Assess Health Care. 2026 Oct 28;42(1):e104. doi:10.1017/s0266462326104310. PMID: 42947537.Checked: Abstract only