Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Unveiling the Disparities in the Field of Precision Medicine: A Perspective
5
Zitationen
9
Autoren
2025
Jahr
Abstract
Background and Aim: Precision medicine prescribes medication based on genetic data, social history, and environment, offering more effective care. This article presumes that many disparities shatter this promise for numerous groups. The article discusses the challenges of personalized medicine in relation to disparities, proposes solutions, and introduces strategies to address these issues. Methods: A comprehensive literature review was done to understand precision medicine, its implementation challenges, and possible answers. Academic databases, including Google Scholar, PubMed, and Scopus, were searched for relevant studies and peer-reviewed articles. Peer-reviewed precision medicine articles in oncology, internal medicine, public health, and obstetrics and gynecology were included to provide a broad and interdisciplinary perspective. Results: The concept of precision medicine needs to be implemented throughout many levels of society and healthcare systems around the world. Access to genomic data is limited in high-income countries, and socioeconomic disparities hinder healthcare equality, particularly for low-income individuals or those without insurance. The digital divide, lack of education, ethical concerns, and regulatory frameworks contribute to disparities in personalized medicine. We believe that health equity will be achieved by addressing these discrepancies and suggesting some strategies to overcome them. Conclusion: Precision medicine has successfully treated and helped in the early detection of many diseases, like severe asthma, cancer, and type 1 diabetes. Socioeconomic position, education, data, access, and regulatory frameworks prevent minorities and low-income populations from using it. For instance, lack of awareness and other inequalities in access to precision medicine limit T1DM HLA typing and autoantibody surveillance. These discrepancies must be addressed to improve minority and low-income T1DM patients' outcomes. The patient-oriented strategy is cost-effective and includes benefits through education, genetic data diversity promotion, and increased access, but regulatory frameworks are essential.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.578 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.470 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.984 Zit.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
2019 · 6.814 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.781 Zit.
Autoren
Institutionen
- Misr University for Science and Technology(EG)
- Military Medical Academy(EG)
- Sana'a University(YE)
- Jerash University(JO)
- University of Baghdad(IQ)
- Al-Azhar University(EG)
- Jordan University of Science and Technology(JO)
- Helwan University(EG)
- Hashemite University(JO)
- General Organization For Teaching Hospitals and Institutes(EG)