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ment. Hence, despite its long-standing history, it has remained inaccessible to healthcare providers and researchers. However, staggering improve-ments in the field of information and communica-tions technology (ICT) and consumer computa-tional hardware have been made in recent years. Hence, big data analysis became accessible to the medical field. Its expanding use should promote better quality and efficiency of healthcare. It is also expected to fuel breakthroughs in medical research and development8).Historically, big data in healthcare revolved around the global transformation toward electronic medical records, including charting, imaging, lab, and billing data. However, medical big data is experiencing a noteworthy evolution regarding its key compo-nents. From the traditional medical and epidemio-logical big data, a paradigm shift is occurring. It is moving toward incorporating personalized multi-omics data and digital phenotypes sourced from mobile health (mHealth) and wearable smart devices2, 3). Traditional medical big data, with its merits, is positioned to reflect aggregate data appropriate for population medicine, evidenced by its use in various epidemiological studies. However, by applying results from population data in indi-vidual care, the variability of patients’ physiology and external factors is hardly considered, ulti-mately leading to suboptimal care. Conversely, a comprehensive data collection on individual subjects sought out by novel medical big data is better suited for P4 medicine. This is since personalized multi-omics and IoMT data can holistically depict a subject’s physiological profile and track one’s current position in various disease spectrums. To truly implement P4 medicine, the healthcare infra-structure must go through a paradigm shift away from the previous “one-size-fits-all” approach to multifaceted, comprehensive approach that incor-porates individualized factors9). Disease Heterogeneity and aim of this studyMost diseases do not exhibit an apparent dichotomy between the healthy and the ill. Diseases are often presented as a spectrum due to their varying presentations, progression (stage) of the under-lying pathology, risk factors, and multiple mecha-nisms to a singular disease10). For instance, dry eye disease (DED) is the most common ocular surface disorder11). It is highly multifactorial and heteroge-nous in its presentation12). DED is affected by an intricate interaction between innumerous environ-mental, host, and including humidity, pollen, the particulate matter under 2.5 microns (PM 2.5), diet, smoking, exercise, contact lens (CL) use, age, sex, family history, and genetics13). Its presentation widely varies; some have less severe symptoms, such as dryness and eye fatigue and others rapidly develop severe symptoms, such as photophobia and permanently decreased visual acuity12, 14). However, despite acknowledging its various pathologic pathways, multifactoriality, and heterogeneous presentation, the current standard of care primarily revolves around a single “gold standard” treatment. The treatment has minimal consideration for drug interaction with individual factors. To effectively resolve this “one-size-fits- all” approach to DED, a previously singular disease must undergo stratification in the context of distinct pathologic pathways, contributing factors, and subjective symptom presentation. This will opti-mize treatment regimens for each disease stratum2).Here, ocular conditions regulated by ocular immune dynamics and inflammatory processes (such as DED and corneal transplantation) are discussed in the context of AI-driven, molecular level, cross-hi-erarchical integrative analysis of medical big data accrued through recent developments in mHealth and multi-omics research. Additionally, we discuss noteworthy revelations in ocular disease patholo-gies and future directions of the newfound value in implementing P4 medicine in the current trend toward ubiquitous, person-oriented medicine.Cross-hierarchical integrative research network and data-driven approachIn understanding the basis of disease heteroge-neity and phenotypes, data-driven biological sciences are at the forefront of medical research. In essence, such an approach starts with collecting robust biological big data, visualizing and extracting essen-tial information, and utilizing the results in solving specific problems2). The sheer amount of data and raw computational power needed for big data anal-ysis has created an obstacle in prior generations of research, and implementation of AI technologies has enabled high-speed and high-accuracy data analysis. For better or worse, traditional hypothe-lifestyle factors, 521