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Abstract

Precision medicine, an innovative approach that tailors medical treatments to individual genetic, environmental, and lifestyle factors, holds great promise for revolutionizing healthcare. By enabling more accurate diagnoses and targeted therapies, it has the potential to improve patient outcomes and reduce unnecessary treatments. However, alongside its clinical benefits, precision medicine raises several ethical concerns.
THE ETHICAL IMPLICATIONS OF PRECISION MEDICINE
Authors
Abram Gracias, Peter Broklyn, Ralph Shad
ABSTRACT
Precision medicine, an innovative approach that tailors medical treatments to individual genetic,
environmental, and lifestyle factors, holds great promise for revolutionizing healthcare. By
enabling more accurate diagnoses and targeted therapies, it has the potential to improve patient
outcomes and reduce unnecessary treatments. However, alongside its clinical benefits, precision
medicine raises several ethical concerns.
Key ethical issues include patient privacy, as genetic data collection can lead to potential
breaches of confidentiality. There is also the risk of discrimination based on genetic information,
both in healthcare access and employment. Furthermore, precision medicine may exacerbate
health inequalities, as advanced therapies may not be equally available to all socio-economic
groups due to high costs. Additionally, concerns about informed consent arise, especially
regarding the complexities of genetic information and the long-term implications of data sharing.
In conclusion, while precision medicine offers substantial advantages for personalized
healthcare, it also presents significant ethical challenges. Addressing these concerns requires
robust policies that balance innovation with privacy, equity, and fairness in healthcare.
INTRODUCTION
Background Information: The Ethical Implications of Precision Medicine
Precision medicine is a rapidly evolving field that integrates genomics, environmental factors,
and lifestyle data to create highly individualized healthcare strategies. Unlike traditional
medicine, which often takes a one-size-fits-all approach, precision medicine seeks to tailor
treatments to the unique characteristics of each patient. Advances in genetic sequencing
technologies, data analytics, and biotechnology have accelerated its development, making it
possible to design therapies that are more targeted and effective for individuals.
This medical revolution is already showing promise in various fields, particularly in oncology,
where treatments are being developed based on a patient’s specific genetic mutations.
Additionally, the use of biomarkers, personalized drug regimens, and gene-editing technologies
like CRISPR offer possibilities for more precise interventions in other conditions, such as
cardiovascular diseases and rare genetic disorders.
However, the shift toward a precision medicine framework introduces complex ethical questions.
The collection and use of extensive genetic and personal health data pose risks to patient privacy
and raise concerns about who has access to this information. As healthcare systems increasingly
rely on genetic data, issues of informed consent become more complicated, particularly as
patients may not fully understand the future implications of sharing their genetic information.
Furthermore, there is a growing concern that precision medicine could lead to genetic
discrimination in healthcare, insurance, and employment, as individuals with certain genetic
profiles might be treated differently.
Another key ethical challenge lies in the potential widening of healthcare disparities. Access to
precision medicine is often limited by high costs, which could lead to unequal access,
particularly for lower-income individuals and communities. Without deliberate policies to
address these disparities, the benefits of precision medicine may only be available to those with
the financial means to afford cutting-edge treatments, further entrenching inequalities in
healthcare.
Thus, while precision medicine offers groundbreaking opportunities for advancing healthcare, it
simultaneously presents significant ethical dilemmas that must be addressed to ensure its
equitable and ethical implementation.
LITERATURE REVIEW
Precision medicine has become a central focus in modern healthcare research due to its potential
to transform patient care. Numerous studies have explored both its medical potential and the
ethical challenges it introduces. This literature review aims to examine the ethical implications
raised by precision medicine as presented in the academic discourse.
Privacy and Data Security
One of the most significant ethical concerns in precision medicine is the issue of patient privacy.
Precision medicine relies heavily on the collection, storage, and analysis of genetic and personal
health data. According to McGuire et al. (2018), genetic data is uniquely sensitive because it not
only provides information about the individual but also their biological relatives. The potential
for data breaches, unauthorized access, or misuse of genetic information raises concerns about
the privacy and security of patients' health information.
Studies by Mittelstadt et al. (2017) further emphasize the risks associated with sharing genetic
data. For instance, genetic information might be used by third parties, including employers or
insurers, to discriminate against individuals based on their genetic predispositions. Although
laws like the Genetic Information Nondiscrimination Act (GINA) exist in the United States,
researchers argue that they may not be sufficient to protect against all forms of discrimination or
data misuse (Fabsitz et al., 2018).
Informed Consent and Autonomy
The issue of informed consent is another topic of ongoing debate in precision medicine.
Traditional models of consent may not adequately cover the complexities involved in genetic
research, where the implications of sharing data may extend far beyond the individual patient.
According to research by Lunshof et al. (2017), obtaining truly informed consent is difficult
when patients may not fully understand how their genetic data could be used in the future,
especially in large-scale databases and biobanks.
Helgesson and Swartling (2019) highlight the concept of "broad consent," which allows data to
be used for a wide range of future research purposes. While this approach is convenient for
research, it can undermine the autonomy of patients who may not be fully aware of the different
ways their data might be utilized.
Equity and Access
Another significant ethical issue identified in the literature is the potential for precision medicine
to exacerbate health disparities. Critics argue that precision medicine, while promising, could
deepen existing inequities in healthcare access due to the high costs associated with genetic
testing and advanced treatments (Tutton, 2019). Studies by Vayena et al. (2020) suggest that
without policies to subsidize or make these services widely available, only affluent populations
will benefit from these advanced therapies.
The literature on this subject consistently points to the need for policies that ensure equitable
access to precision medicine. Research conducted by Ruger et al. (2020) emphasizes that high-
income countries are better positioned to implement and benefit from precision medicine, while
low- and middle-income countries may face barriers due to lack of resources and infrastructure.
This growing divide raises questions about fairness in global healthcare.
Genetic Discrimination
Concerns about genetic discrimination are also prevalent in the literature. Research by Rothstein
(2018) illustrates that the possibility of employers or insurers using genetic information to make
discriminatory decisions remains a pressing issue. Although some legal frameworks, such as the
aforementioned GINA, offer protection, they are not foolproof, and gaps in legislation exist
globally (Williams et al., 2019).
Ethical Frameworks and Policy Development
Various ethical frameworks have been proposed to guide the responsible use of precision
medicine. As argued by Juengst et al. (2016), a balance must be struck between innovation and
protecting individual rights. The need for transparent governance and policy development is
emphasized in the literature to mitigate the risks associated with precision medicine. Timmons
and MacDonald (2018) propose a patient-centered approach that prioritizes informed consent,
data security, and equitable access.
In conclusion, while precision medicine offers significant benefits for personalized healthcare,
the ethical implications surrounding privacy, informed consent, access, and discrimination are
complex and far-reaching. The literature indicates a need for continuous policy reform and
ethical scrutiny to ensure that precision medicine is both equitable and respectful of individual
rights.
METHODOLOGY
This section outlines the research methods used to explore the ethical implications of precision
medicine, focusing on data collection, analysis, and ethical considerations.
Research Design
The study adopts a qualitative research design to investigate the ethical challenges of precision
medicine, as qualitative methods are ideal for exploring complex issues such as privacy,
autonomy, equity, and discrimination. This approach enables an in-depth understanding of the
perspectives of key stakeholders, including patients, healthcare providers, policymakers, and
ethicists.
Data Collection
1. Literature Review
An extensive literature review was conducted to gather secondary data from academic journals,
books, and policy reports. The review focused on studies published between 2015 and 2024,
covering topics such as privacy, informed consent, genetic discrimination, and healthcare equity
in the context of precision medicine. Databases such as PubMed, Scopus, and Google Scholar
were used to locate peer-reviewed articles.
2. Interviews
Semi-structured interviews were conducted with a purposive sample of 20 individuals, including
medical practitioners, bioethicists, policymakers, and patients who have undergone genetic
testing or personalized treatments. The participants were selected based on their involvement or
experience with precision medicine to ensure diverse perspectives. The interview guide included
questions about the benefits and ethical concerns of precision medicine, focusing on privacy,
consent, and access to healthcare.
Sample size: 20 participants
Interview duration: 45-60 minutes
Interview format: In-person and virtual interviews via Zoom
3. Case Studies
Three case studies of healthcare institutions that have integrated precision medicine into their
practice were conducted. These case studies explored how hospitals and clinics manage ethical
concerns such as patient data privacy and the equitable distribution of personalized treatments.
The institutions were selected based on their experience with precision medicine, geographic
diversity, and their policy frameworks for ethical governance.
Data Analysis
1. Thematic Analysis
Data from interviews and case studies were analyzed using thematic analysis. Transcriptions of
the interviews were coded using NVivo software to identify recurring themes related to privacy,
informed consent, and healthcare access. The themes were then categorized into broader ethical
issues to understand the underlying concerns and their implications for the implementation of
precision medicine.
2. Document Analysis
Policy documents and regulatory guidelines on precision medicine, such as those from the World
Health Organization (WHO) and national healthcare systems, were reviewed. This analysis
helped to identify existing ethical frameworks and assess their effectiveness in addressing
concerns raised by precision medicine practices.
Ethical Considerations
1. Informed Consent
All interview participants were provided with detailed information about the study’s objectives
and methodology. Informed consent was obtained from each participant before data collection.
Participants were informed about their right to withdraw from the study at any time.
2. Confidentiality
To protect the privacy of the interviewees, all personally identifiable information was
anonymized. Interview transcripts and case study data were stored securely, and access was
restricted to the research team.
3. Ethical Approval
The study obtained ethical clearance from the institutional review board (IRB) of the researchers'
affiliated institution, ensuring that all research activities adhered to ethical standards for human
subjects research.
Limitations
This study is limited by its focus on qualitative methods, which may not capture the full scope of
ethical concerns in precision medicine. The small sample size of interviewees may also limit the
generalizability of the findings. Additionally, the study focuses on ethical issues in developed
countries, which may differ from those in low- and middle-income nations where access to
precision medicine is more restricted.
RESULTS
The results of the study provide insights into the ethical concerns associated with precision
medicine based on the analysis of interviews, case studies, and literature. The findings highlight
key issues related to privacy, informed consent, equity, and discrimination, as well as propose
considerations for addressing these challenges.
Privacy and Data Security
Interviews: Participants expressed significant concerns about the privacy and security of genetic
data. Many emphasized the risk of data breaches and unauthorized access, particularly as genetic
information is sensitive and can have far-reaching implications. Concerns were raised about the
effectiveness of current security measures and the potential for misuse of data by third parties
such as insurers or employers.
Case Studies: Institutions implementing precision medicine reported varying levels of data
protection practices. While some had robust systems in place for data security and privacy,
others faced challenges in ensuring that all data handling procedures were consistently followed.
The case studies revealed that even with stringent policies, the risk of data breaches remains a
persistent concern.
Informed Consent and Autonomy
Interviews: The issue of informed consent emerged as a critical concern. Participants noted that
patients often struggle to understand the complexities of genetic information and the potential
future uses of their data. The concept of "broad consent" was debated, with some arguing that it
facilitates research while others felt it undermines patient autonomy by not fully addressing the
implications of data use.
Case Studies: The case studies highlighted diverse approaches to obtaining informed consent.
Institutions varied in their strategies for communicating the potential uses of genetic data and
ensuring that patients were fully aware of what they were consenting to. Some institutions
implemented additional measures, such as follow-up consultations, to enhance patient
understanding.
Equity and Access
Interviews: There was a consensus among participants that precision medicine could exacerbate
existing health disparities. High costs associated with genetic testing and personalized treatments
were identified as barriers to access, particularly for underprivileged populations. Participants
emphasized the need for policies to ensure equitable distribution of precision medicine benefits.
Case Studies: The case studies illustrated that while some institutions had initiatives aimed at
reducing disparities, access to precision medicine often remained limited to those who could
afford it. Institutions that provided financial assistance or subsidized testing were noted for their
efforts to improve equity, but the overall impact on healthcare access was still uneven.
Genetic Discrimination
Interviews: Concerns about genetic discrimination were prominent among participants. The
potential for genetic information to be used against individuals in insurance and employment
contexts was highlighted as a major issue. While some legal protections exist, there was
skepticism about their adequacy in preventing all forms of discrimination.
Case Studies: The case studies showed that institutions were aware of the risks of genetic
discrimination and had implemented policies to address these concerns. However, the
effectiveness of these measures varied, and there was a call for stronger regulatory frameworks
to better protect individuals from potential discrimination.
Summary of Findings
1. Privacy and Data Security: There are significant concerns about the security of genetic
data and the potential for unauthorized access and misuse. Institutions vary in their data
protection practices, with ongoing risks despite robust measures.
2. Informed Consent and Autonomy: Ensuring truly informed consent is challenging,
with patients often lacking full understanding of the implications of sharing their genetic
data. Broad consent models are controversial, with varying approaches to enhancing
patient comprehension.
3. Equity and Access: Precision medicine has the potential to deepen existing health
disparities, with high costs limiting access for lower-income populations. While some
institutions are making efforts to improve access, significant barriers remain.
4. Genetic Discrimination: There are concerns about genetic discrimination in insurance
and employment, with existing legal protections deemed insufficient by some
participants. Institutions are implementing policies to mitigate discrimination, but more
robust regulatory measures are needed.
DISCUSSION
The findings from this study underscore the transformative potential of precision medicine in
tailoring healthcare to individual needs. However, they also reveal several significant ethical
challenges that must be addressed to ensure that the benefits of precision medicine are realized in
a manner that respects patient rights and promotes equity.
Privacy and Data Security
Implications: The study highlights that privacy and data security are paramount concerns in the
implementation of precision medicine. The sensitivity of genetic information necessitates robust
security measures to protect against unauthorized access and potential misuse. Despite existing
safeguards, the risk of data breaches remains a significant concern.
Discussion: Given the sensitive nature of genetic data, the implementation of advanced
encryption technologies and stringent access controls is critical. Additionally, ongoing audits and
updates to security practices can help mitigate risks. However, institutions must also consider the
ethical implications of potential breaches and develop transparent policies for managing and
responding to data security incidents.
Informed Consent and Autonomy
Implications: The study reveals that traditional models of informed consent may not be
sufficient for precision medicine, given the complexity and potential future uses of genetic data.
Broad consent models, while facilitating research, may not adequately address patient autonomy
or ensure that individuals fully understand the implications of their consent.
Discussion: To enhance informed consent, it is essential to provide clear, comprehensible
information about the potential uses of genetic data and the implications of participation.
Institutions should consider implementing ongoing consent processes, where patients are
regularly updated about new uses or findings related to their data. Educational resources and
decision-support tools can also help patients make more informed choices.
Equity and Access
Implications: The study identifies a significant concern that precision medicine could exacerbate
health disparities. High costs and limited availability of advanced treatments may restrict access
to only those with sufficient financial means, thus potentially widening the gap between different
socio-economic groups.
Discussion: Addressing equity in precision medicine requires policy interventions to make
advanced therapies and genetic testing more accessible. This could include subsidies, insurance
coverage, and public health initiatives aimed at reducing costs and improving access for
underserved populations. Additionally, efforts to ensure that precision medicine benefits are
distributed equitably across different demographic groups are crucial for reducing disparities.
Genetic Discrimination
Implications: The potential for genetic discrimination remains a significant concern. Despite
legal protections, there is skepticism about the adequacy of current measures to prevent all forms
of discrimination related to genetic information.
Discussion: Strengthening legal frameworks and regulatory oversight can help address the issue
of genetic discrimination. Policymakers should work to close gaps in existing protections and
ensure that individuals' genetic information is not used to their detriment in insurance,
employment, or other areas. Furthermore, institutions should foster a culture of ethical awareness
and support for individuals facing potential discrimination.
Integrative Ethical Frameworks
Implications: The study suggests that a comprehensive approach is needed to address the ethical
challenges of precision medicine. This includes integrating ethical considerations into the design
and implementation of precision medicine practices and policies.
Discussion: Developing integrative ethical frameworks involves collaboration among
stakeholders, including researchers, clinicians, policymakers, and patient advocates. These
frameworks should address privacy, consent, equity, and discrimination concerns holistically.
Ongoing ethical review and adaptation of policies as the field evolves are necessary to ensure
that precision medicine continues to align with ethical standards and societal values.
CONCLUSION
Precision medicine represents a significant advancement in healthcare, offering the potential for
highly personalized treatments and improved patient outcomes. However, as this field evolves, it
brings forth a range of ethical challenges that must be carefully addressed to ensure that its
benefits are realized equitably and responsibly.
Summary of Key Findings
1. Privacy and Data Security: The sensitivity of genetic information necessitates robust
data protection measures. Despite existing safeguards, the risk of breaches and misuse
remains a concern. Advanced security technologies and transparent policies are essential
to safeguard patient data and address potential breaches.
2. Informed Consent and Autonomy: Traditional models of consent may not fully capture
the complexities of genetic data usage. Broad consent models, while useful for research,
can undermine patient autonomy. Enhanced consent processes that include clear
communication and ongoing updates are necessary to ensure that patients are fully
informed and their autonomy is respected.
3. Equity and Access: Precision medicine has the potential to exacerbate existing health
disparities due to high costs and limited availability. Addressing these disparities requires
policy interventions to make advanced treatments and genetic testing more accessible to
all socio-economic groups. Equitable distribution of precision medicine benefits is crucial
for reducing health inequalities.
4. Genetic Discrimination: The risk of genetic discrimination persists despite existing legal
protections. Strengthening legal frameworks and regulatory oversight is needed to
prevent discrimination and protect individuals from potential misuse of their genetic
information.
Implications for Practice
To address these ethical challenges, the following actions are recommended:
Enhance Data Security: Implement advanced encryption and access controls, conduct
regular security audits, and establish clear protocols for managing data breaches.
Improve Informed Consent: Develop clear, comprehensible consent forms and provide
ongoing education about the implications of genetic data use. Implement regular updates
and consultations to ensure informed decision-making.
Promote Equity: Advocate for policies that reduce the cost barriers to precision
medicine and ensure equitable access for all patients. Support initiatives that address
health disparities and improve access to advanced treatments.
Strengthen Protections Against Discrimination: Review and enhance legal protections
against genetic discrimination. Promote ethical practices and awareness within
institutions to support individuals facing potential discrimination.
Future Directions
The field of precision medicine will continue to evolve, and ongoing ethical scrutiny is essential.
Future research should focus on:
Developing innovative approaches to data protection and privacy.
Exploring new models of informed consent that address the complexities of genetic data.
Evaluating the impact of policy interventions on healthcare equity and access.
Assessing the effectiveness of legal and regulatory measures in preventing genetic
discrimination.
By addressing these ethical considerations proactively, the field of precision medicine can
advance in a manner that aligns with ethical principles, respects patient rights, and promotes
equitable healthcare outcomes.
REFERENCES
1. Carrasco-Ramiro, F., Peiró-Pastor, R., & Aguado, B. (2017). Human genomics projects
and precision medicine. Gene therapy, 24(9), 551-561.
2. Cardon, L. R., & Harris, T. (2016). Precision medicine, genomics and drug
discovery. Human molecular genetics, 25(R2), R166-R172.
3. Aronson, S. J., & Rehm, H. L. (2015). Building the foundation for genomics in precision
medicine. Nature, 526(7573), 336-342.
4. Ahmed, Z., Zeeshan, S., Mendhe, D., & Dong, X. (2020). Human gene and disease
associations for clinicalgenomics and precision medicine research. Clinical and
translational medicine, 10(1), 297-318.
5. MacRae, C. A., & Vasan, R. S. (2016). The future of genetics and genomics: closing the
phenotype gap in precision medicine. Circulation, 133(25), 2634-2639.
6. Udegbe, F. C., Ebulue, O. R., Ebulue, C. C., & Ekesiobi, C. S. (2024). Precision
Medicine and Genomics: A comprehensive review of IT-enabled
approaches. International Medical Science Research Journal, 4(4), 509-520.
7. DeGroat, W., Abdelhalim, H., Patel, K., Mendhe, D., Zeeshan, S., & Ahmed, Z. (2024).
Discovering biomarkers associated and predicting cardiovascular disease with high
accuracy using a novel nexus of machine learning techniques for precision
medicine. Scientific reports, 14(1), 1.
8. Zeggini, E., Gloyn, A. L., Barton, A. C., & Wain, L. V. (2019). Translational genomics
and precision medicine: Moving from the lab to the clinic. Science, 365(6460), 1409-
1413.
9. Padmanabhan, S., & Dominiczak, A. F. (2021). Genomics of hypertension: the road to
precision medicine. Nature Reviews Cardiology, 18(4), 235-250.
10. Nakagawa, H., & Fujita, M. (2018). Whole genome sequencing analysis for cancer
genomics and precision medicine. Cancer science, 109(3), 513-522.
11. Ahmed, Z., Zeeshan, S., Mendhe, D., & Dong, X. (2020). Human gene and disease
associations for clinicalgenomics and precision medicine research. Clinical and
translational medicine, 10(1), 297-318.
12. Khodadadian, A., Darzi, S., Haghi-Daredeh, S., Sadat Eshaghi, F., Babakhanzadeh, E.,
Mirabutalebi, S. H., & Nazari, M. (2020). Genomics and transcriptomics: the powerful
technologies in precision medicine. International Journal of General Medicine, 627-640.
13. Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507-
522.
14. Williams, A. M., Liu, Y., Regner, K. R., Jotterand, F., Liu, P., & Liang, M. (2018).
Artificial intelligence, physiological genomics, and precision medicine. Physiological
genomics, 50(4), 237-243.
15. Ahmed, Z., Zeeshan, S., Mendhe, D., & Dong, X. (2020). Human gene and illness
connections for clinical genomics and precision medicine studies. Clinical and
Translational Medicine, 10, 297-318.
16. Ginsburg, G. S., & Phillips, K. A. (2018). Precision medicine: from science to
value. Health affairs, 37(5), 694-701.
17. Peck, R. W. (2018). Precision medicine is not just genomics: the right dose for every
patient. Annual review of pharmacology and toxicology, 58(1), 105-122.
18. Álvarez-Machancoses, Ó., DeAndres Galiana, E. J., Cernea, A., Fernández de la Viña, J.,
& Fernández-Martínez, J. L. (2020). On the role of artificial intelligence in genomics to
enhance precision medicine. Pharmacogenomics and personalized medicine, 105-119.
19. Xu, J., Yang, P., Xue, S., Sharma, B., Sanchez-Martin, M., Wang, F., ... & Parikh, B.
(2019). Translating cancer genomics into precision medicine with artificial intelligence:
applications, challenges and future perspectives. Human genetics, 138(2), 109-124.
20. Mumtaz, H., Saqib, M., Jabeen, S., Muneeb, M., Mughal, W., Sohail, H., ... & Ismail, S.
M. (2023). Exploring alternative approaches to precision medicine through genomics and
artificial intelligencea systematic review. Frontiers in Medicine, 10, 1227168.
21. Dong, X., Kong, D., Mendhe, D., & Bergren, S. M. (2019). Leveraging technology to
improve health disparity research: trilingual data collection using tablets. Journal of the
American Geriatrics Society, 67(S3), S479-S485.
22. Khoury, M. J., Bowen, S., Dotson, W. D., Drzymalla, E., Green, R. F., Goldstein, R., ...
& Bunnell, R. (2022). Health equity in the implementation of genomics and precision
medicine: A public health imperative. Genetics in Medicine, 24(8), 1630-1639.
23. Dainis, A. M., & Ashley, E. A. (2018). Cardiovascular precision medicine in the
genomics era. JACC: Basic to Translational Science, 3(2), 313-326.
24. Au, T. H., Wang, K., Stenehjem, D., & Garrido-Laguna, I. (2017). Personalized and
precision medicine: integrating genomics into treatment decisions in gastrointestinal
malignancies. Journal of Gastrointestinal Oncology, 8(3), 387.
25. Khoury, M. J., Iademarco, M. F., & Riley, W. T. (2016). Precision public health for the
era of precision medicine. American journal of preventive medicine, 50(3), 398.
26. Harris, C., Tang, Y., Birnbaum, E., Cherian, C., Mendhe, D., & Chen, M. H. (2024).
Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of
Novel Digital Technologies. Archives of Clinical Neuropsychology, 39(3), 290-304.
27. Roychowdhury, S., & Chinnaiyan, A. M. (2013). Advancing precision medicine for
prostate cancer through genomics. Journal of Clinical Oncology, 31(15), 1866-1873.
28. Cantor, J. C., Mouzon, D., Hu, W., Bergren, S., Yedidia, M., Cohen, S., ... & Duberstein,
P. (2023). Health Implications of Enduring and Emerging Stressors: Design of the New
Jersey Population Health Cohort (NJHealth) Study. Available at SSRN 4615490.
29. Omidiran, O., Patel, A., Usman, S., Mhatre, I., Abdelhalim, H., DeGroat, W., ... &
Ahmed, Z. (2024). GWAS advancements to investigate disease associations and
biological mechanisms. Clinical and translational discovery, 4(3), e296.
30. Mendhe, D., Dogra, A., Nair, P. S., Punitha, S., Preetha, K. S., & Babu, S. B. T. (2024,
April). AI-Enabled Data-Driven Approaches for Personalized Medicine and Healthcare
Analytics. In 2024 Ninth International Conference on Science Technology Engineering
and Mathematics (ICONSTEM) (pp. 1-5). IEEE.
31. Agarwala, V., Khozin, S., Singal, G., O’Connell, C., Kuk, D., Li, G., ... & Abernethy, A.
P. (2018). Real-world evidence in support of precision medicine: clinico-genomic cancer
data as a case study. Health affairs, 37(5), 765-772.
32. Aung, K. L., Fischer, S. E., Denroche, R. E., Jang, G. H., Dodd, A., Creighton, S., ... &
Knox, J. J. (2018). Genomics-driven precision medicine for advanced pancreatic cancer:
early results from the COMPASS trial. Clinical Cancer Research, 24(6), 1344-1354.
33. Juengst, E., McGowan, M. L., Fishman, J. R., & Settersten Jr, R. A. (2016). From
“personalized” to “precision” medicine: the ethical and social implications of rhetorical
reform in genomic medicine. Hastings Center Report, 46(5), 21-33.
34. Kessler, C. (2018). Genomics and precision medicine: implications for critical
care. AACN Advanced Critical Care, 29(1), 28-35.
35. Dominiczak, A., Delles, C., & Padmanabhan, S. (2017). Genomics and precision
medicine for clinicians and scientists in hypertension. Hypertension, 69(4), e10-e13.
36. Conway, J. R., Kofman, E., Mo, S. S., Elmarakeby, H., & Van Allen, E. (2018).
Genomics of response to immune checkpoint therapies for cancer: implications for
precision medicine. Genome medicine, 10, 1-18.
37. Ahmed, Z., Zeeshan, S., Mendhe, D., & Dong, X. (2020). Human gene and disease
associations for clinicalgenomics and precision medicine research. Clinical and
translational medicine, 10(1), 297-318.
... However, even while precision medicine promises innovative possibilities for improving healthcare, it also poses serious moral conundrums that need to be resolved to guarantee it's just and moral application. 48 ...
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