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522sis-driven biological research takes quantized advancements toward a goal. Initial research on a disease starts with identifying molecular candi-dates, followed by separate cellular, tissue, and organism level investigations toward elucidating portions of a single pathology. Recently, due to the rapid advancements in ICT and computational hardware, multi-dimensional integrative methodol-ogies became accessible through implementing AI in demanding tasks such as big data analysis. In the scheme of viewing AI-driven biological data analysis as “AI = algorithm + big data,” the devel-opment of the latter appears to be the bottleneck. Establishing secure and robust data accrual routes remain a challenge for utilizing AI-driven research at its full potential.Invisible medicine and mobile healthWithin the realm of IoMT, mHealth and wear-able smart devices have received attention for their capability to provide “invisible medicine” through collecting important physiological data through biosensors and various input data without inter-rupting activity2, 3). mHealth is defined as “as medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” by the Global Observatory for eHealth (GOe) of the World Health Organization (WHO)15). mHealth can be a valuable tool for informing patients with evidence-based medicine and motivating self-management. It can also be a platform for providers to collect real-time, objective, medical, and lifestyle data to create opti-mized treatment for users2, 16, 17). mHealth-based cross sectional study for hetero-geneity of dry eye diseaseDED is a multifactorial disorder affected by three broad individualized factors: host-related, lifestyle, and environmental factors13). These factors interact with various mediators of its pathway, causing a varying degree of predisposition to DED in individ-uals. While the medical field widely accepts its multifactoriality and individual variability of DED presentation, an approach to provide personalized regimens remains to be explored2). This is further complicated by the wide variety of symptoms asso-ciated with DED, including dryness, photophobia, eye fatigue, decreased visual acuity. These symp-toms are often neglected as non-ocular or nonspe-cific symptoms. This leads to a wide underdiag-nosis of DED, causing delayed treatment and a worse prognosis on initial diagnosis12). Therefore, a prompt, comprehensive investigation is needed to visualize and stratify the heterogeneous DED presentations effectively. It must be followed by optimization of the treatment regimen for each disease stratum. An individualized, preventative, and predictive strategy may be employed to delay or stop the onset altogether with better insight into specific factors associated with each stratum2).We released an in-house iPhone application, DryEyeRhythm, in November 2016 using the ResearchKit platform to collect real-world data and perform large-scale crowdsourced clinical research on subjective symptoms and lifestyle factors asso-ciated with DED12, 14, 17-19). An English version of DryEyeRhythm was also released in November 2017 to widen the userbase. Previous reports indi-cate that smartphone application-based crowd-sourced clinical research is well-suited for early detection and long-term management of chronic diseases, such as DED and diabetes mellitus12, 17, 20, 21). Along with detailed demographic and lifestyle information, DryEyeRhythm allows data collection on DED-related subjective symptoms and depres-sion screening results. This is done with the Japa-nese version of the Ocular Surface Disease Index (J-OSDI)17, 22-26) and Self-rating Depression Scale (SDS), respectively12, 17, 18). Additionally, as our previous investigations showed a positive correla-tion between tear film break-up time (TFBUT) and maximum blink intervals (MBI), a blink interval measurement function is provided as a simple DED screening tool in DryEyeRhythm24, 27).In our large-scale crowdsourced clinical research using DryEyeRhythm, we identified numerous contributory factors of DED aggravation through big data analysis17). This study included 5,265 users between November 2016 and November 2017 and odds ratios (95% confidence interval) on individual aggravative factors and DED subjective symptoms. The identified factors include younger age (every 1 year): 0.99 (0.98-0.99), female sex: 1.85 (1.60-2.14), collagen disease: 2.81 (1.34-5.90), depression: 1.68 (1.23-2.29), current use of CL: 1.24 (1.09-1.41), hay fever: 1.18 (1.04-1.33), extended screen time