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Global Translational Medicine Personalized, multi-omics disease detection

The integration of wearable technology into clinical teaching of these novel techniques to health care providers
settings is currently being discussed for its potential is underway, but there is still a long way to go. It is crucial
scalability. In fact, clinical trials have a long history of to consider integrating omics analysis and interpretation
incorporating wearable technology, even before the advent into medical education, as it is becoming increasingly
of modern wearables. Devices such as continuous heart important to align curricula with evolving technologies.
monitors, oxygen saturation monitors, and other traditional This highlights the need to recognize the essential role of
health tools have been essential for data collection. data science and AI in healthcare and incorporate these
They enable the seamless tracking of vital signs, activity disciplines into academic portfolios. By doing so, we can
levels, medication adherence, and symptom progression, ensure a comprehensive and future-ready approach to
significantly enhancing the quality and efficiency of medical training.
clinical trials. Wearable tech furthermore minimizes the In addition, these technologies come with an upfront
need for frequent in-person visits, reducing the burden on price tag that might seem unsurmountable for many
participants and making trials more accessible to diverse strained health-care systems. However, as has already
populations. The expanded use of digital tools available been mentioned, these techniques are becoming more
to measure and monitor health parameters is one key economically sustainable with their advancements.
enabler to decentralized clinical trials, where those tools Furthermore, an upfront investment might save a
are used for recruitment, for example, screening, to safety large sum of money downstream, exemplified by many
monitoring and outcome measures. Wearable devices prophylactic measures implemented in public health and
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offer the potential in early detection of adverse events, screening programs. With that being said, and as just one
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which aids in improving patient safety and enhancing illustrative example, fewer than one in four individuals
accuracy of trial results, thereby enriching the clinical trial with or at risk of CVD in the U.S. use wearable devices,
experience. with older age, lower educational attainment and lower

5. Challenges and future directions household income associated with lower likelihoods of
use. Strategies are needed to ensure equitable adoption to
While the promise of true personalized medicine is avoid exacerbating disparities. 77
tantalizing, several challenges remain. Patient compliance It is important to highlight that we will probably resolve
over time, data privacy, ethical considerations, data the technical challenges in the near future, but addressing
integrity and ownership, robust data generation, and the the ethical and societal challenges will require further
need for robust data integration platforms are among the considerations of multiple stakeholders from research,
key hurdles to overcome. Accidental findings are a palpable governments, health-care providers, private sector,
risk that needs to be handled upfront and ahead of time. 30,73 patients, etc. and dedicated funding and development of
When undergoing the comprehensive evaluation outlined solutions that limit the risks and protect the individuals.
herein, there is a possibility that previously unknown
health risks, such as cancer, as well as other variables Extending beyond diagnostics and monitoring, in true
such as pregnancy or illicit drug use may be, detected. personalized medicine, therapies are meticulously crafted
It is important to note that the participant may not have based on an individual’s personal and longitudinal data.
requested nor wish to know such information and certainly For example, cancer treatments can be customized to target
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may not want others to be privy to it. We also need to be specific genetic mutations or protein signatures that
able to understand the complex data that is generated to change during progression of the disease, 79,80 maximizing
achieve robustness, precision, and interpretability—by efficacy while minimizing side effects. Similarly,
no means a simple or trivial feat, and we are still a long medications for chronic conditions can be optimized to
way from standardized bioinformatic solutions. This ties match an individual’s metabolism and response, ensuring
into data FAIRification (making data Findable, Accessible, the best possible outcomes. Going beyond diagnosis
Interoperable, and Reusable), which is another challenge to predicting treatments is a huge leap, which needs
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to overcome. The generation of extensive multi-omics data considerable effort from medical research fields.
yields limited benefits if such datasets lack standardization, In summary, the era of personalized diagnostics and
comparability, thorough annotation, and easy accessibility, treatments represents a transformative shift in healthcare.
hindering their integration and reuse. Furthermore, While challenges in data privacy, ethical considerations
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integrating such rich and complex data into routine clinical and integration persist, the prospect of tailoring therapies
practice necessitates a shift in medical education, research, to an individual’s molecular blueprint holds promise.
and health-care infrastructure, not to mention the Overcoming hurdles in education, infrastructure and cost
validation and the development of robust guidelines. The will be crucial for realizing the full potential of personalized

Volume 3 Issue 1 (2024) 5 https://doi.org/10.36922/gtm.2357