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cells. Subsequently, holistic data analysis on Treg genomic expression in response to inflammatory and immune system activation through multi-omics and AI machine learning techniques could specify the molecular dynamics underlying corneal allograft rejection and DED pathogenesis. Homing and stabi-lizing Treg function on the ocular surface may help better understand the immune cell differentiation pathways and therapeutic targets. In addition, deeper insight into the previously unknown mech-anisms of immune cell responses to external factors and cells’ end characteristics could reveal molec-ular details that accurately detect pathologic gene subtypes, contributing to DED or corneal allograft rejection. These mechanisms ultimately uncover novel inflammatory and immune system targets.The medical infrastructure outlined by the Society 5.0 plan enables patient- and population- oriented medicine, focusing on preventative and lifetime healthcare within one’s daily life. Cross-hi-erarchical data-driven analysis strategies inte-grating mHealth and multi-omics data appear promising in elucidating disease heterogeneity and its underlying pathology. This is imperative in implementing the principles of P4 medicine. The current healthcare paradigm revolves around facil-ity-based care. A patient- and population- oriented healthcare infrastructure that melds seamlessly into daily life remains to be fully established. Advancements are made in a nonintrusive, longitu-dinal collection of medical big data due to the increasing penetrance of wearable smart devices and IoT devices. There is a lack of analysis and evidence that allows practical application of the collected data into disease-preventative behavior modification and intervention. Therefore, societal efforts should promote research utilizing the collected medical big data, including our investiga-tions on large-scale mHealth-based crowdsourced clinical research2, 3, 12, 14, 17-19). The findings must inte-grate to lay the groundwork for implementation.Recent clinical evidence suggests that subjective findings reported by patients, in addition to clinicians, are valuable in understanding health outcomes25). Patients’ own report of health status, also known as patient-reported outcomes (PROs), have presented challenges for providers. There is an ongoing discussion on effective ways to merge PROs within the traditional healthcare operation toward patient care and communication60). Conve-niently, mHealth takes a unique position. It is a tool for nonintrusive lifestyle data collection and a plat-form for users reporting ePROs (such as subjec-tive symptoms)61). Prompt distribution of collected evidence and its implications to the population of interest was a longtime challenge for public health care agencies. We developed a framework that promotes participatory medicine by allowing real-time access to the aggregate data collected from our in-house smartphone application in a public webpage3). The ease of establishing a feedback structure through mHealth and AI analysis, which can continuously collect individual data on subjec-tive symptoms and lifestyle factors followed by prompt analysis report to the population, can have significant implications in providing evidence-based and ubiquitous healthcare under the princi-ples of P4 medicine2, 3).Digital phenotyping is another subject that has received attention in mHealth62). As the sensors and inputs attached to smartphones and wearable smart devices continue to diversify, research on converting the gathered user inputs into useful biomarkers can provide a new aspect into one’s health status. This perspective can differentiate in the stratification, visualization, and individualiza-tion of complex disease presentations. The resul-tant findings can then be utilized in preventative, predictive, and personalized medicine. Especially concerning the demand on nonintrusive healthcare that functions within one’s daily life during the ongoing- and post-SARS-CoV-2 pandemic era63, 64), digital phenotyping with mHealth devices may help push the boundaries of the prospective global healthcare paradigm.As medical big data on individual multi-omics and mHealth continues to accumulate in ocular disease research, new findings on ocular patholo-gies suggest improvements in diagnosis and treat-ment. In advancing ocular diseases, such data may play a critical role in innovative clinical research. Results show promise in stratifying heterogeneous presentations of previously singular ocular diseases through mHealth and in elucidating a comprehen-sive understanding of disease pathogenesis through integrating single-cell analysis with omics data. 525Discussion