Myles Joshua Toledo Tan ^{1,2,3,4,5,6,7*} Harishwar Reddy Kasireddy ^1,8 Alfredo Bayu Satriya ¹ Hezerul Abdul Karim ^9* Nouar AlDahoul ¹⁰

• ¹ Department of Electrical and Computer Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States
• ² Department of Epidemiology, College of Public Health & Health Professions and College of Medicine, University of Florida, Gainesville, Florida, United States
• ³ Biology Program, College of Arts and Sciences, University of St. La Salle, Bacolod City, Negros Occidental, Philippines
• ⁴ Department of Natural Sciences, College of Arts and Sciences, University of St. La Salle, Bacolod City, Negros Occidental, Philippines
• ⁵ Department of Chemical Engineering, College of Engineering and Technology, University of St. La Salle, Bacolod City, Negros Occidental, Philippines
• ⁶ Department of Electronics Engineering, College of Engineering and Technology, University of St. La Salle, Bacolod City, Negros Occidental, Philippines
• ⁷ Yo-Vivo Corporation, Bacolod City, Negros Occidental, Philippines
• ⁸ Division of Nephrology, Hypertension and Renal Transplantation – Quantitative Health Section, Department of Medicine, College of Medicine, University of Florida,, Gainesville, Florida, United States
• ⁹ Faculty of Engineering, Multimedia University, Cyberjaya, Selangor Darul Ehsan, Malaysia
• ¹⁰ Department of Computer Science, Division of Science, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates

Hyper-personalized medicine represents the cutting edge of healthcare, which aims to tailor treatment and prevention strategies uniquely to each individual. Unlike traditional approaches, which often adopt a one-size-fits-all or even broadly personalized approach based on broad genetic categories, hyper-personalized medicine considers an individual’s comprehensive health data by integrating unique biological, genetic, lifestyle, and environmental influences. This method goes beyond simple genetic profiling by recognizing that health outcomes are influenced by complex interactions among our environment, daily routines, and physiological processes and responses. Central to hyper-personalized medicine is the integration of lifestyle and environmental factors. Lifestyle habits, such as diet (Dalwood et al., 2020; Genel et al., 2020; Marx et al., 2020; Hepsomali & Groeger, 2021; Dinu et al., 2022; Yang et al., 2022; Sadler et al., 2024), exercise (Chow et al., 2022; Qiu et al., 2022; Ross et al., 2022; D’Onofrio et al., 2023; Isath et al., 2023; Mahindru et al., 2023; Ashcroft et al., 2024; Ponzano et al., 2024), and sleep patterns (Hepsomali & Groeger, 2021; Baranwal et al., 2023; Eshera et al., 2023; Lim et al., 2023; Sletten et al., 2023; Uccella, 2023; Weinberger et al., 2023), directly impact health. Hence, understanding these factors helps tailor interventions that align with the day-to-day realities of an individual. Environmental factors, such as air quality (Cheek et al., 2020; Markandeya et al., 2020; Shukla et al., 2022; Tang et al., 2022; Abdul-Rahman et al., 2024; Bedi & Bhattacharya, 2024), climate (Coates et al., 2020; Ebi et al., 2021; Helldén et al., 2021; Reismann et al., 2021; Rocque et al., 2021; Zhang et al., 2021; Münzel et al., 2024; Palmeiro-Silva et al., 2024), and exposure to pollutants (Qadri & Faiq, 2019; Petroni et al., 2020; Lin et al., 2022; Sun et al., 2022; Xu et al., 2022; Yu et al., 2022; Levin et al., 2023; Shetty et al., 2023; Deziel & Villanueva 2024; Sharma et al., 2024), also play significant roles in determining health outcomes. By continuously monitoring and analyzing these elements, healthcare providers can create dynamic health plans that adapt to real-time changes. This would allow for proactive measures and optimized care. To enable such a complex model of care, advanced technologies like quantum computing, artificial general intelligence (AGI), internet of things (IoT), and 6G connectivity play crucial roles. Quantum computing offers the ability to process vast and intricate datasets, such as those required to model interactions between genetic markers, environmental exposures, and lifestyle choices, with far greater speed and accuracy than classical computing (Munshi et al., 2023; Kumar et al., 2024; Stefano, 2024; Ullah & Garcia-Zapirain, 2024; Yu et al., 2024). AGI, with its adaptive learning capabilities, can analyze and make sense of this data to provide precise, evolving recommendations that change as a patient’s environment or lifestyle does (Liu et al., 2024; Mitchell, 2024; Sun et al., 2024; Tu et al., 2024). IoT devices, including wearables and environmental sensors, gather continuous data from individuals, tracking physical activity, biometrics, and environmental conditions like air quality and humidity (Puri et al., 2021; Islam et al., 2024; Mathkor et al., 2024; Rocha et al., 2024; Šajnović et al., 2024; Salam, 2024). With the advent of 6G connectivity, this data is seamlessly transferred and processed in real time, enabling instant feedback and intervention (Nayak & Patgiri, 2021; Nguyen et al., 2021; Ahad et al., 2024; Kumar, Kaur, et al., 2024; Mahmood et al., 2024; Mihovska et al., 2024). Together, these technologies form the backbone of a hyper-personalized healthcare model, which will push beyond traditional medical practices to create a highly responsive, individual-centered approach to health. As these advancements continue to evolve, hyper-personalized medicine has the potential to fundamentally reshape healthcare, offering truly personalized interventions that support long-term health and well-being.