Multimodal Artificial Intelligence for Parkinson's Disease: Designing Integration Around Missing Data, Clinical Context, and Uncertainty

Abstract Voice, movement, handwriting, imaging, molecular assays, and clinical history offer different views of Parkinson's disease. Combining them is attractive because no individual measurement captures every relevant dimension. Yet adding modalities also introduces asynchronous observations, unequal access, correlated errors, and missing data that can undermine apparent gains in predictive performance. This selected narrative review uses original studies and primary methodological publications to examine how multimodal Parkinson's research should be designed. Targeted searches were performed on 16 September 2026; no systematic search, pooled effect estimate, or new participant analysis is claimed. We distinguish evidence about functional expression, biological state, and clinical outcomes, and propose that each modality retain its measurement context before integration. Early, late, and intermediate fusion are compared as practical design choices rather than a hierarchy of sophistication. A proposed evaluation matrix examines participant separation, modality availability, additional value over simpler baselines, calibration, and the consequences of requesting another measurement. Missingness is treated as both a statistical problem and a property of the care pathway. The article develops a staged research agenda for NeuralCipher in which low-burden assessments, specialist investigations, and explicit abstention are evaluated as parts of a defined workflow. These proposals are unvalidated and do not describe established NeuralCipher capabilities. The principal conclusion is that useful multimodal integration depends on knowing when additional information changes a defensible decision, not simply on whether a larger model improves an internal discrimination score. Article details Authors: NeuralCipherai; Kadir Tamrak; Salih Yaldız; Feride Yaldız; Ömer Ağyol; Yavuz Selim Silay; Hasan Randa Publisher: neluracipher.ai DOI: 10.5281/zenodo.22779021 Version: 1.0Language: English Project website: https://neuralcipher.ai References Adams, J. L., Kangarloo, T., Tracey, B., O’Donnell, P., Volfson, D., Latzman, R. D., Zach, N., Alexander, R., Bergethon, P., Cosman, J., Anderson, D., Best, A., Severson, J., Kostrzebski, M. A., Auinger, P., Wilmot, P., Pohlson, Y., Waddell, E., Jensen-Roberts, S., . . . the Parkinson Study Group Watch-PD Study Investigators and Collaborators. (2023). Using a smartwatch and smartphone to assess early Parkinson’s disease in the WATCH-PD study. npj Parkinson's Disease, 9(1), Article 64. https://doi.org/10.1038/s41531-023-00497-x Bot, B. M., Suver, C., Neto, E. C., Kellen, M., Klein, A., Bare, C., Doerr, M., Pratap, A., Wilbanks, J., Dorsey, E. R., Friend, S. H., & Trister, A. D. (2016). The mPower study, Parkinson disease mobile data collected using ResearchKit. Scientific Data, 3(1), Article 160011. https://doi.org/10.1038/sdata.2016.11 Choi, H., Ha, S., Im, H. J., Paek, S. H., & Lee, D. S. (2017). Refining diagnosis of Parkinson's disease with deep learning-based interpretation of dopamine transporter imaging. NeuroImage: Clinical, 16, 586–594. https://doi.org/10.1016/j.nicl.2017.09.010 Collins, G. S., Moons, K. G. M., Dhiman, P., Riley, R. D., Beam, A. L., Van Calster, B., Ghassemi, M., Liu, X., Reitsma, J. B., van Smeden, M., Boulesteix, A.-L., Camaradou, J. C., Celi, L. A., Denaxas, S., Denniston, A. K., Glocker, B., Golub, R. M., Harvey, H., Heinze, G., . . . Logullo, P. (2024). TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ, 385, Article e078378. https://doi.org/10.1136/bmj-2023-078378 Drotár, P., Mekyska, J., Rektorová, I., Masarová, L., Smékal, Z., & Faundez-Zanuy, M. (2016). Evaluation of handwriting kinematics and pressure for differential diagnosis of Parkinson's disease. Artificial Intelligence in Medicine, 67, 39–46. https://doi.org/10.1016/j.artmed.2016.01.004 Hällqvist, J., Bartl, M., Dakna, M., Schade, S., Garagnani, P., Bacalini, M.-G., Pirazzini, C., Bhatia, K., Schreglmann, S., Xylaki, M., Weber, S., Ernst, M., Muntean, M.-L., Sixel-Döring, F., Franceschi, C., Doykov, I., Śpiewak, J., Vinette, H., Trenkwalder, C., . . . Mollenhauer, B. (2024). Plasma proteomics identify biomarkers predicting Parkinson’s disease up to 7 years before symptom onset. Nature Communications, 15(1), Article 4759. https://doi.org/10.1038/s41467-024-48961-3 Latourelle, J. C., Beste, M. T., Hadzi, T. C., Miller, R. E., Oppenheim, J. N., Valko, M. P., Wuest, D. M., Church, B. W., Khalil, I. G., Hayete, B., & Venuto, C. S. (2017). Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: A longitudinal cohort study and validation. The Lancet Neurology, 16(11), 908–916. https://doi.org/10.1016/s1474-4422(17)30328-9 Little, M. A., McSharry, P. E., Hunter, E. J., Spielman, J., & Ramig, L. O. (2009). Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. IEEE Transactions on Biomedical Engineering, 56(4), 1015–1022. https://doi.org/10.1109/tbme.2008.2005954 Marek, K., Chowdhury, S., Siderowf, A., Lasch, S., Coffey, C. S., Caspell‐Garcia, C., Simuni, T., Jennings, D., Tanner, C. M., Trojanowski, J. Q., Shaw, L. M., Seibyl, J., Schuff, N., Singleton, A., Kieburtz, K., Toga, A. W., Mollenhauer, B., Galasko, D., Chahine, L. M., . . . the Parkinson's Progression Markers Initiative. (2018). The Parkinson's progression markers initiative (PPMI) – establishing a PD biomarker cohort. Annals of Clinical and Translational Neurology, 5(12), 1460–1477. https://doi.org/10.1002/acn3.644 Nalls, M. A., McLean, C. Y., Rick, J., Eberly, S., Hutten, S. J., Gwinn, K., Sutherland, M., Martinez, M., Heutink, P., Williams, N. M., Hardy, J., Gasser, T., Brice, A., Price, T. R., Nicolas, A., Keller, M. F., Molony, C., Gibbs, J. R., Chen-Plotkin, A., . . . Singleton, A. B. (2015). Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: A population-based modelling study. The Lancet Neurology, 14(10), 1002–1009. https://doi.org/10.1016/s1474-4422(15)00178-7 Postuma, R. B., Berg, D., Stern, M., Poewe, W., Olanow, C. W., Oertel, W., Obeso, J., Marek, K., Litvan, I., Lang, A. E., Halliday, G., Goetz, C. G., Gasser, T., Dubois, B., Chan, P., Bloem, B. R., Adler, C. H., & Deuschl, G. (2015). MDS clinical diagnostic criteria for Parkinson's disease. Movement Disorders, 30(12), 1591–1601. https://doi.org/10.1002/mds.26424 Schalkamp, A.-K., Peall, K. J., Harrison, N. A., & Sandor, C. (2023). Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis. Nature Medicine, 29(8), 2048–2056. https://doi.org/10.1038/s41591-023-02440-2 Siderowf, A., Concha-Marambio, L., Lafontant, D.-E., Farris, C. M., Ma, Y., Urenia, P. A., Nguyen, H., Alcalay, R. N., Chahine, L. M., Foroud, T., Galasko, D., Kieburtz, K., Merchant, K., Mollenhauer, B., Poston, K. L., Seibyl, J., Simuni, T., Tanner, C. M., Weintraub, D., . . . Soto, C. (2023). Assessment of heterogeneity among participants in the Parkinson's Progression Markers Initiative cohort using α-synuclein seed amplification: A cross-sectional study. The Lancet Neurology, 22(5), 407–417. https://doi.org/10.1016/s1474-4422(23)00109-6 Vickers, A. J., & Elkin, E. B. (2006). Decision curve analysis: A novel method for evaluating prediction models. Medical Decision Making, 26(6), 565–574. https://doi.org/10.1177/0272989x06295361 Yang, Y., Yuan, Y., Zhang, G., Wang, H., Chen, Y.-C., Liu, Y., Tarolli, C. G., Crepeau, D., Bukartyk, J., Junna, M. R., Videnovic, A., Ellis, T. D., Lipford, M. C., Dorsey, R., & Katabi, D. (2022). Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals. Nature Medicine, 28(10), 2207–2215. https://doi.org/10.1038/s41591-022-01932-x