Digital technologies are increasingly integrated into smart healthcare systems, enabling new forms of data-driven assessment and clinical decision support in neuropsychiatric practice. Digital biomarkers derived from behavioral, cognitive, and physiological signals collected through mobile devices, wearable sensors, and digital platforms provide continuous and ecologically valid indicators of symptom dynamics. At the same time, computational inference methods, including statistical modeling and machine learning, support the integration and interpretation of these heterogeneous data sources within clinical workflows. This paper examines how digital biomarkers and computational inference can be integrated into smart neuropsychiatric healthcare systems to enhance assessment, monitoring, and clinical decision-making. Rather than viewing artificial intelligence as an autonomous diagnostic tool, the analysis conceptualizes it as a set of computational mechanisms that transform raw digital signals into structured clinical insights. A three-component framework is proposed, consisting of biomarker extraction, inferential modeling, and clinical decision support integration. Biomarker extraction identifies clinically relevant digital features from real-world data streams. Inferential modeling synthesizes multimodal biomarkers to characterize symptom profiles and predict risk trajectories. Clinical decision support systems embed these inferences within healthcare workflows to assist clinicians in triage, monitoring, and personalized care planning. By situating digital biomarkers and computational inference within the broader context of smart healthcare infrastructures, this framework clarifies how artificial intelligence contributes to scalable, data-driven, and patient-centered neuropsychiatric assessment. The study highlights practical implications for integrating digital phenotyping into electronic health records, telemedicine platforms, and remote monitoring systems, thereby advancing the development of intelligent and adaptive mental healthcare environments.
References
[1] Torous, J., Onnela, J. P., Keshavan, M. (2017) New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices. Translational Psychiatry, 7(3), e1053-e1053.
[2] Torous, J., Ledley, K. T., Gorban, C., Strudwick, G., Schwarz, J., Choudhary, S., Kochhar, S. (2025) Accelerating digital mental health: the society of digital psychiatry’s three-pronged road map for education, digital navigators, and AI. JMIR Mental Health, 12(1), e84501.
[3] Oudin, A., Maatoug, R., Bourla, A., Ferreri, F., Bonnot, O., Millet, B., Adrien, V. (2023) Digital phenotyping: data-driven psychiatry to redefine mental health. Journal of Medical Internet Research, 25, e44502.
[4] McGinnis, E. W., Cherian, J., McGinnis, R. S. (2024) The state of digital biomarkers in mental health. Digital Biomarkers, 8(1), 210-217.
[5] Davidson, B. I. (2022) The crossroads of digital phenotyping. General Hospital Psychiatry, 74, 126-132.
[6] Parziale, A., Mascalzoni, D. (2022) Digital biomarkers in psychiatric research: data protection qualifications in a complex ecosystem. Frontiers in Psychiatry, 13, 873392.
[7] Friston, K. (2023) Computational psychiatry: from synapses to sentience. Molecular Psychiatry, 28(1), 256-268.
[8] Castro Martínez, J. C., Santamaría-García, H. (2023) Understanding mental health through computers: an introduction to computational psychiatry. Frontiers in Psychiatry, 14, 1092471.
[9] Hitchcock, P. F., Fried, E. I., Frank, M. J. (2022) Computational psychiatry needs time and context. Annual Review of Psychology, 73, 243-270.
[10] Spadaro, B., Martin-Key, N. A., Bahn, S. (2021) Building the digital mental health ecosystem: opportunities and challenges for mobile health innovators. Journal of Medical Internet Research, 23(10), e27507.
[11] Lee, K., Lee, T. C., Yefimova, M., Kumar, S., Puga, F., Azuero, A., Dionne-Odom, J. N. (2023) Using digital phenotyping to understand health-related outcomes: a scoping review. International Journal of Medical Informatics, 174, 105061.
[12] Zarate, D., Stavropoulos, V., Ball, M., de Sena Collier, G., Jacobson, N. C. (2022) Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry, 22(1), 421.
[13] Marsch, L. A. (2021) Digital health data-driven approaches to understand human behavior. Neuropsychopharmacology, 46(1), 191-196.
[14] Singh, O. P. (2023) Artificial intelligence in the era of ChatGPT – opportunities and challenges in mental health care. Indian Journal of Psychiatry, 65(3), 297-298.
[15] Wu, H., Li, M. D. (2025) Digital psychiatry: concepts, framework, and implications. Frontiers in Psychiatry, 16, 1572444.
[16] Alsalloum, G., Dalibalta, S., Hadijat, Y. (2025) Harnessing digital health interventions to address the heterogeneity of depression: a systematic review. Frontiers in Digital Health, 7, 1654745.
[17] Singhal, S., Cooke, D. L., Villareal, R. I., Stoddard, J. J., Lin, C. T., Dempsey, A. G. (2024) Machine learning for mental health: applications, challenges, and the clinician’s role. Current Psychiatry Reports, 26(12), 694-702.
[18] Muñoz, J. M., Borbón, D., Bezerra, A. M. (2024) Computational psychiatry and digital phenotyping: ethical and neurorights implications. Developments in Neuroethics and Bioethics, 7, 49-63.
[19] Alfalahi, H., Dias, S. B., Khandoker, A. H., Chaudhuri, K. R., Hadjileontiadis, L. J. (2023) A scoping review of neurodegenerative manifestations in explainable digital phenotyping. NPJ Parkinson’s Disease, 9(1), 49.
[20] Paquin, V., Ferrari, M., Sekhon, H., Rej, S. (2023) Time to think “meta”: a critical viewpoint on the risks and benefits of virtual worlds for mental health. JMIR Serious Games, 11, e43388.
[21] D’Alfonso, S., Coghlan, S., Schmidt, S., Mangelsdorf, S. (2025) Ethical dimensions of digital phenotyping within the context of mental healthcare. Journal of Technology in Behavioral Science, 10(1), 132-147.
[22] Shaw, A. D., Sumner, R. L., Berndt, L. C. (2025) Predictive coding and neurocomputational psychiatry: a mechanistic framework for understanding mental disorders. Frontiers in Psychiatry, 16, 1713833.
[23] Tun, H. M., Rahman, H. A., Naing, L., Malik, O. A. (2025) Trust in artificial intelligence-based clinical decision support systems among health care workers: systematic review. Journal of Medical Internet Research, 27, e69678.
[24] Machleid, F., Michnevich, T., Huang, L., Schröder-Frerkes, L., Wiegmann, C., Muffel, T., Kaminski, J. (2024) Remote measurement based care (RMBC) interventions for mental health – Protocol of a systematic review and meta-analysis. Plos one, 19(2), e0297929.
[25] Borsboom, D. (2017) A network theory of mental disorders. World Psychiatry, 16(1), 5-13.
[26] Smith, R., Badcock, P., Friston, K. J. (2021) Recent advances in the application of predictive coding and active inference models within clinical neuroscience. Psychiatry and Clinical Neurosciences, 75(1), 3-13.
[27] Tse, G., Lee, Q., Chou, O. H. I., Chung, C. T., Lee, S., Chan, J. S. K., Zhou, J. (2024) Healthcare big data in Hong Kong: development and implementation of artificial intelligence-enhanced predictive models for risk stratification. Current Problems in Cardiology, 49(1), 102168.
[28] Onnela, J. P. (2021) Opportunities and challenges in the collection and analysis of digital phenotyping data. Neuropsychopharmacology, 46(1), 45-54.
[29] Bzdok, D., Meyer-Lindenberg, A. (2018) Machine learning for precision psychiatry: opportunities and challenges. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(3), 223-230.
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Mou, D. (2025) Digital Biomarkers and Computational Inference in Smart Neuropsychiatric Healthcare. Journal of Disease and Public Health, 1(3), 25-34. https://doi.org/10.71052/jdph/MSHO1980