Adaptive Trust Scoring in Decentralized Identity Systems Using Generative AI for Digital Marksheet Verification in Education

Introduction

Cancer is a leading cause of death worldwide, with millions of lives lost annually due to limited treatment efficacy, variability in patient response, and the complexity of cancer biology. Current cancer treatment approaches face significant limitations, especially when treatments do not align with individual patient needs. Pharmacogenomics—tailoring treatments based on genetic factors—has emerged as a promising approach to address these issues. However, implementing pharmacogenomics at scale in healthcare faces challenges, including data privacy, lack of individual control over health data, and limited personalization.

        Integrating Self-Sovereign Identity (SSI) and generative AI offers a new framework for enhancing pharmacogenomic cancer treatment. SSI enables secure, patient-controlled data management, while generative AI can provide advanced, personalized insights based on patient-specific genetic information. This integrated approach could empower cancer treatment by allowing for highly personalized therapies while protecting patient autonomy and privacy.

Literature Review

Pharmacogenomics aims to optimize drug efficacy and minimize adverse effects by tailoring treatments based on genetic profiles. Several advances have been made in pharmacogenomic cancer treatment, but limitations persist:

1. Personalized Cancer Therapy: Pharmacogenomics has been used in various cancers to match drugs with genetic mutations. For instance, mutations in the HER2 gene affect breast cancer therapy [1-2], while the BRAF gene has been a target in melanoma treatment [3].

2. Precision Medicine in Oncology: Genetic tests, like those for the EGFR mutation in lung cancer, guide targeted treatments to improve survival rates. Despite these successes, current pharmacogenomics often fails to consider the complex, multi-gene interactions in cancer, leading to mixed efficacy [4-5].

3. Challenges and Limitations:

1. Data Privacy and Security: Pharmacogenomic data, stored in centralized databases, is vulnerable to breaches, raising concerns over privacy and misuse [6-7].
2. Limited Patient Autonomy: Patients often have limited control over their genetic data once it is stored, which raises ethical and regulatory concerns, especially under frameworks like HIPAA and GDPR [8].
3. Data Management Complexities: The vast amount of genetic data required for pharmacogenomics demands sophisticated data-handling technology, which is often challenging to scale [9-10].

Integrated Self-Sovereign Identity and Generative AI in Pharmacogenomic Cancer Treatment

An empowered pharmacogenomic treatment system for cancer, integrating SSI and generative AI, could address the limitations of current pharmacogenomics in several key ways:

1. Enhanced Data Privacy and Security Through SSI: Self-sovereign identity provides a decentralized identity management model, allowing patients to retain control over their genetic data. By limiting access to authorized parties only, SSI mitigates risks associated with centralized data storage. Patients can share their data selectively, enhancing privacy and supporting ethical data usage [11-13].

2. Generative AI for Personalized Treatment Optimization: Generative AI can analyze complex genetic data and recognize patterns beyond traditional methods, tailoring treatment plans according to individual needs. By identifying gene interactions and disease characteristics unique to each patient, generative AI offers insights into effective drug combinations and optimal dosages [14-16].

3. Integration of SSI and AI for Efficient Data Management: By combining SSI with generative AI, the proposed system offers a scalable, decentralized, and privacy-preserving approach to pharmacogenomic cancer treatment. Patients can securely share their data across healthcare entities, while AI processes the information to provide real-time treatment recommendations [17-18].

Challenges of Implementing Empowered Pharmacogenomic Treatment

Despite its potential, implementing an empowered pharmacogenomic treatment system using SSI and generative AI in healthcare presents various challenges:

1. Technical and Interoperability Challenges: Integrating SSI across diverse healthcare networks requires high interoperability, which may be difficult to achieve given the variation in existing systems [19-20].

2. Data Compliance and Security: Ensuring compliance with regulations like GDPR and HIPAA remains essential, as well as addressing the potential risk of data breaches in decentralized systems [21-22].

3. AI Model Bias and Validation: Generative AI models must be rigorously tested to avoid biases that could skew treatment recommendations. A biased or unvalidated model could lead to inappropriate therapies, which could harm patients rather than help them [23-24].

4. Cost and Infrastructure Needs: Developing and maintaining this advanced infrastructure, particularly for SSI and generative AI, can be costly. This could present a challenge in resource-limited healthcare settings, where advanced computational and data-sharing resources may be lacking [25-26].

Conclusion and Future Directions

Integrating SSI and generative AI into pharmacogenomic cancer treatment offers a promising solution for delivering safe, personalized therapies. As the healthcare landscape evolves, future research should focus on improving system interoperability, ethical AI model training, and data-sharing protocols that respect patient autonomy. In the future, we may see an empowered healthcare model where patients have control over their data, and treatment strategies are tailored to their unique genetic profiles.

References

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2. Nishimoto, A., et al. “HER2-Targeted Cancer Therapy.” Cancer Treatment Reviews, 2021.

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5. Meric-Bernstam, F., et al. “Challenges in Precision Oncology.” Journal of Cancer Research, 2021.

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11. Sun, C., et al. “SSI for Data Privacy in Healthcare.” Health Affairs, 2022.

12. Triggle, C. R., et al. “Blockchain and SSI in Precision Medicine.” IEEE Journal of Biomedical and Health Informatics, 2022.

13. Evans, P., et al. “Decentralized Data Solutions in Healthcare.” Journal of Health Informatics, 2021.

14. Tran, Q., et al. “Generative AI in Cancer Therapy.” Frontiers in Digital Health, 2023.

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16. Lopez, C., et al. “AI-Driven Pharmacogenomics.” Journal of Personalized Medicine, 2021.

17. Rowley, A., et al. “Data Interoperability in Genomics.” Journal of Digital Health, 2022.

18. Chiu, S., et al. “Using AI and SSI in Genomic Data Management.” Bioinformatics Advances, 2021.

19. Li, D., et al. “Technical Barriers to Data Integration in Healthcare.” Healthcare Technology Letters, 2022.

20. Chen, H., et al. “Interoperability Issues in Genomic Medicine.” Computational Biology, 2023.

21. Roberts, S., et al. “Data Compliance in AI-Based Healthcare.” American Journal of Medical Ethics, 2022.

22. Foster, M., et al. “Data Security Challenges in Genomic Data.” Journal of Biomedical Informatics, 2023.

23. Baig, F., et al. “Addressing Bias in Generative AI Models.” AI in Healthcare, 2023.

24. Williams, K., et al. “Validating AI Models for Precision Medicine.” Journal of Medical Research, 2022.

25. Singh, M., et al. “Cost Implications of AI and SSI in Genomics.” Health Economics Journal, 2021.

26. Gupta, A., et al. “Infrastructure Needs in Personalized Medicine.” Trends in Biotechnology, 2022.