Imagine walking into a store and being handed a single shirt labeled “one size fits all.” Chances are, it wouldn’t fit you perfectly. Yet, this is essentially how traditional healthcare treats patients, providing the same treatments to everyone, despite biological differences that can lead to drastic changes in medical needs. Personalized and precision medicine seeks to revolutionize healthcare by customizing treatments to each person’s distinct genetic makeup, lifestyle, and environment. (Kishi., et al, 2024). This approach can improve health outcomes, especially for complex conditions such as cancer, by evaluating unique DNA markers. (ACS, 2025). Unfortunately, high costs, limited access to advanced testing, and the technology required means not everyone may benefit equally without careful implementation. (Badr, Yara et al, 2025). To truly revolutionize personalized medicine, we must guarantee not only that it is selective and efficient, but that it is fair.

Unlike traditional treatment designed for the “average” patient, precision care treats individuals based on how diseases uniquely affect them, resulting in better outcomes. (MedlinePlus, 2022). By studying a person’s genetics, environment, and lifestyle, doctors can avoid reactive treatments with potentially severe side effects and prescribe effective medication. (CDC, 2024). A recent example of disease-specific precision treatment is uniQure’s gene therapy AMAT-130. While still in clinical trials, the treatment has shown promising results–the first effective Huntington’s treatment. Researchers found that high-dose patients showed a 75 percent slower disease progression than low-dose patients. This trial employed a “one and done” style treatment that won’t permanently alter DNA, steering clear of present gene editing risk. (G. Katherine, 2025). AI-driven personalized medicine is already showing productive outcomes, with deep learning models achieving physician-level accuracy in diagnosing skin cancer for over 5 million Americans annually. Similarly, in cardiology, AI is being used to predict risks and assist in diagnosing heart disease, potentially benefiting approximately 30 million adults in the U.S. (Gatla, 2024). This demonstrates how, with help from AI, personalized medicine has more accurate medical judgments than ever based on data analysis, a key goal of translational biology. In addition to skin cancer and cardiology, QPCR data identified a three-gene signature predicting how cervical cancer patients will respond to treatment. Researchers suggest this low-cost, RNA-based approach could help personalize therapies and improve outcomes to other diseases. (Marchiano et al., 2021). By tailoring treatment and diagnosis to each individual, we move beyond the curbs of traditional, “one-size-fits-all” medicine. This innovation holds great potential for individuals with complex or rare diseases, where traditional diagnostic methods often fall short due to broad approaches to healthcare. A good scenario of this is in cancer patients that undergo targeted therapy which can be tailored to attack certain cancer biomarkers or target areas that help the spread of the cancer meanwhile minimizing damage to healthy cells using your cancer’s specialized genomics. (American Cancer Society, 2025). Ultimately, this approach reduces healthcare imbalances and improves outcomes across diverse patient groups. (Hosni., et al, 2025).

While personalized medicine has great potential, its widespread adoption is restricted by economic and technological barriers, particularly the high cost of genome sequencing. (Wetterstrand, 2023). According to the National Human Genome Institute, sequencing an entire human genome still costs around $600 (Wetterstrand, 2023). Even as sequencing becomes cheaper, integrating these technologies into everyday clinical settings involves costly infrastructure, staff , and data systems, especially for low-resource settings. (Wetterstrand, 2023). The promise of personalized medicine is threatened by disparities in access to costly genetic testing and advanced diagnostic technologies, which could worsen existing health inequalities. For example, the high expense of wearable health devices and other advanced tools limits their availability to individuals with lower socioeconomic status, potentially creating new gaps in healthcare if these technologies are applied without consideration of equity (Babu et al., 2024). This concern also applies to genetic tests and modern diagnostics, which remain out of reach for many. Without careful attention to ensure equal access, these innovations may mainly benefit wealthier patients, leaving others behind with delayed diagnoses.

Compounding inequity, privacy concerns grow as individuals’ identities can be pieced together through software-based systems. Scholars warn that the increasing volume of sensitive health data could lead to privacy breaches, discrimination, and even patients avoiding care due to fear of exposure as personalized care moves into clinical settings. Even authorized access to electronic health records may be overly broad, giving providers unnecessary access to disregarded genetic information. (Abul-Husn and Eimear, 2019). Data-driven models can match current healthcare standards using AI, but require large, diverse, and secure datasets for accuracy and privacy. Researchers stress that it must be treated the same as precision medicine data is recorded with as many different types of data from little people as it should be with plenty of data with plenty of people to ensure accuracy to make advancements in patient care along with clinical results (Velmovitsky et al., 2021). As the healthcare system integrates expensive technologies and policies, investments in care are essential to prevent precision medicine from being a one-sided technology and instead make it a tool for reducing health imbalances across diverse communities.

Personalized medicine could potentially transform healthcare by shifting from one-size-fits-all treatments to care fit for each individual’s unique genetic profile, lifestyle habits, and environment. This shift leads to better diagnoses, targeted therapies, fewer side effects, and improved outcomes, especially for complex or rare diseases. However, despite these advances, significant challenges remain. High costs, limited access to advanced technology, and concerns about privacy threaten to restrict the benefits of personalized medicine to only a privileged handful. If these barriers fail to be addressed, existing health disparities may widen even further, leaving disadvantaged and rural populations behind. To fully grasp the benefits of personalized medicine, there must be investments in making genetic testing and innovative diagnostics affordable and accessible to all. This means expanding infrastructure, training providers, making policies, and protecting data privacy. Only through right and equal opportunity can personalized medicine reach its full potential as a groundbreaking force for better and fairer healthcare worldwide.

References

Abul-Husn, Noura S, and Eimear E Kenny. “Personalized Medicine and the Power of Electronic

Health Records.” National library of medicine 177,1 (2019) : 58-69.

Babu, Mohan., et al. “Wearable Devices: Implications for Precision Medicine and the

Future of Health Care.” Annual Review of Medicine 75 (2024) : 401-415.

Badr, Yara et al. “The Use of Big Data in Personalized Healthcare to Reduce Inventory Waste

and Optimize Patient Treatment.” Journal of personalized medicine 14,4 (2024) 383.

Francisco, Kishi., et al. “Can Personalized Medicine Coexist with Health Equity?

Examining the Cost Barrier and Ethical Implications.” ArVix 1 (2024) : 1-30.

Gaither, Katherine. “Breakthrough in Huntington’s Disease Treatment Shows Unprecedented

Results for Patients – Heersink School of Medicine News.” Uab.edu

Gatla, Teja R., “An Innovative Study Exploring Revolutionizing Healthcare With

AI:Personalized Medicine: Predictive Diagnostic Techniques And Individualized Treatment.” International Journal of Advanced Research and Interdisciplinary Scientific Endeavours 2 (2024) : 61-70.

Marchiano, Ruggero D., et al. “Translational Research in the Era of Precision Medicine: Where

We Are and Where We Will Go.” Journal of Personalized Medicine 11 (2021) : 1-20.

,Velmovitsky, Pedro E., et al. “Convergence of Precision Medicine and Public Health Into

Precision Public Health: Toward a Big Data Perspective.” Frontiers in Public Health 9 (2021) : 1-17.

Wetterstrand, Kris A. “DNA Sequencing Costs: Data” NHGRI Genome Sequencing Program.

16 May. 2023. <https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data&gt;

(23 Sep. 2025).

“​Precision Health: Predict and Prevent Disease” (2024, November 13). Genomics and Your

Health, 13 Nov. 2024. <https://www.cdc.gov/genomics-and-health/precision-health-predict/index.html&gt;

(25 Sep. 2025).

“How Does Targeted Therapy Work?” Cancer.org, 2024,

<www.cancer.org/cancer/managing-cancer/treatment-types/targeted-therapy/how-does-targeted-therapy-work.html.> (28 Sep. 2025).

“What Is Precision Medicine?: MedlinePlus Genetics.” Medlineplus.gov, 22 Sept.

2020, <https://medlineplus.gov/genetics/understanding/precisionmedicine/definition/&gt;

(4 Oct. 2025).

“Targeted Therapy.” American Cancer Society, 2 June. 2025, <https://www.cancer.org/cancer/managing-cancer/treatment-types/targeted-therapy.html&gt;

(3 Oct. 2025).