By: Syed Raiyan Ali – syedraiyanali@gmail.com; Department of computer science and Engineering( Data Science ), Student of computer science and Engineering( Data Science ), Madanapalle Institute Of Technology and Science, 517325, Angallu , Andhra Pradesh.
ABSTRACT –
The Transformation impacts of Artificial Intelligence ( AI ) on personalized medicine and its potential to change healthcare outcomes. AI technologies, ranging from data analysis and interpretation to diagnostic tool and treatment planning, offer unrivalled opportunities for modified medical interventions to individual patient characteristics. Through sophisticated algorithms, AI does the analysis of complex biological data, predicts disease risks, and enhances the diagnostic accuracy. Furthermore, AI-powered personalized medicine promises to expand access to high quality health care and points global health disparities. However, challenges such ass data privacy, bias and regulatory changes must be addressed to ensure the responsible integration of AI into health care practices. This article underscores the importance of interdisciplinary collaboration, ethical consideration and policy-making efforts in harnessing AI’s potential to advance personalized medicine responsibly.
INTRODUCTION-
Targeted therapy is a new dimension for personalized medicine unlike the days when everyone treated the same skin diseases regardless of his/her color. It shows how different diseases can affect us in singular manner according to our heredity among other factors like social life. The way AI has advanced has made it one of the most important breakthroughs in this field by providing new methods and tools which can be used to develop more specific therapies[1]. Health providers can use big data with sophisticated algorithms to predict adverse events, customize interventions and enhance survivorship rates for patients better today than never before.
THE ROLE OF AI IN PERSONALIZED MEDICIN
DATA ANALYSIS AND INTERPRETATION
the growth of biotechnology depends on artificial intelligence (AI) that can interpret vast amounts of data generated through bio informatics, which is a significant feature of various biomedical research methods leading to complexity and diversity within biological systems. AI supports, among others, machine learning, neural networks and natural language processing (NLP), all of which have paramount importance in this area as they help spot patterns and associations that may remain unnoticed by human analysts[2].
ENHANCING HEALTHCARE ACCESSIBILITY
AI-based personalized medicine stands out among the many promises because it is likely to result in equal access to health services[3]. For instance, AI could take over data analysis tasks and diagnostic techniques that would otherwise be carried out by humans, thereby reducing pressure on healthcare systems with an aim of making them more available and less expensive for different kinds of people. It will also help bridge global health gaps by giving people advanced medical care regardless of where they come from or their financial means.
ETHICAL AND REGULATORY CHALLENGES
A number of issues arise for integrating AI in the health care and personalize treatment areasand those challenges are first, machine learning applications in precision medicine need to consider data privacy issues to protect patient confidentiality. Second, algorithmic bias in AI is another key challenge[4]; if it is not managed well, it may cause health disparities among race which may lead to poor treatment outcomes. In addition to this, the synthetic control framework for AI in health care is still taking shape hence calling for clear policy frameworks that will facilitate secure and efficient introduction[5]. And there are many other challenges that might occur while integrating AI, as represented in table 1 with their potential solutions.
CHALENGES | DESCRIPTION | POTENTIAL SOLUTION |
Data Privacy | Making sure patients’ individual medical records are secret and secure. | Implement powerful encryption methods, anonymize the data and establish too strict rules for data governance. |
Bias in AI Algorithms | There is a chance that AI systems may unknowingly maintain or make worse any biases that already exist, resulting in different results of treatment. | Develop and implement bias detection and mitigation techniques, and ensure diverse and representative data sets. |
Regulatory Compliance | The changing regulation will be issue for AI in health care require clear guidelines and standards. | Approach regulatory organizations to develop comprehensive frameworks, and ensure compliance through regular audits. |
Transparency and Explainability | The AI algorithms often function as “ black boxes ” making it difficult to understand their decision making processes. | Develop models in such a way that they can provide clear documents and rationale for AI driven decisions. |
Continual Learning and Adaptation | Update the AI model with technology and ensure ongoing education for health care professionals. | Develop a continuous training programs, updates for frame work frequently and support lifelong learning for practitioners. |
Table 1: Representing the Challenges that might occur while integrating AI in personalized medicine and there possible solutions
INTERDISCIPLINARY COLLABORATION AND POLICY-MAKING
To achieve a successful integration of artificial intelligence into personalized medicine, cooperation among different disciplines including health care professionals, data analysts, ethicists and legal makers is needed. There is importance in constructing ethical frameworks and regulatory policies that will maintain the equilibrium between innovation and patient safety[6]. Additionally, there should be interdisciplinary collaborations which are aimed at designing AI applications which are known to be transparent, fair and accountable with an intention of ensuring that the advantages of personalized medicine can be attained without violating any code of ethics whatsoever[7].
CONCLUSION-
The area of personalized medicine is going to be completely changed by AI; it provides an amazing chance of customizing treatments according to patient peculiarities. By enhancing data analysis, diagnostic accuracy, and treatment planning, AI can significantly improve healthcare outcomes and expand access to high-quality care. Nevertheless, it is imperative that issues relating to data privacy, bias as well as regulation are confronted head-on in order for AI technologies to be integrated responsibly into health care systems. As such interdisciplinary collaboration and policy-making will be important to utilize predictive modeling within machine learning appropriately in advancing personalized healthcare thus bringing forth a time when caring about much or little would not matter.
REFERENCES-
- A. A. Seyhan and C. Carini, “Are innovation and new technologies in precision medicine paving a new era in patients centric care?,” J. Transl. Med., vol. 17, no. 1, p. 114, Apr. 2019, doi: 10.1186/s12967-019-1864-9.
- T. Alqahtani et al., “The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research,” Res. Soc. Adm. Pharm., vol. 19, no. 8, pp. 1236–1242, Aug. 2023, doi: 10.1016/j.sapharm.2023.05.016.
- C. Carini and A. A. Seyhan, “Tribulations and future opportunities for artificial intelligence in precision medicine,” J. Transl. Med., vol. 22, no. 1, p. 411, Apr. 2024, doi: 10.1186/s12967-024-05067-0.
- L. Cheng, K. R. Varshney, and H. Liu, “Socially Responsible AI Algorithms: Issues, Purposes, and Challenges,” J. Artif. Intell. Res., vol. 71, pp. 1137–1181, Aug. 2021, doi: 10.1613/jair.1.12814.
- K. T. Putra, A. Z. Arrayyan, R. Z. Syahputra, Y. A. Pamungkas, and M. Rahaman, “Design a Two-Axis Sensorless Solar Tracker Based on Real Time Clock Using MicroPython,” Emerg. Inf. Sci. Technol., vol. 4, no. 1, Art. no. 1, May 2023, doi: 10.18196/eist.v4i1.18697.
- R. Bommu, “Ethical Considerations in the Development and Deployment of AI-powered Medical Device Software: Balancing Innovation with Patient Welfare,” J. Innov. Technol., vol. 5, no. 1, Art. no. 1, May 2022.
- B. D. Alfia, A. Asroni, S. Riyadi, and M. Rahaman, “Development of Desktop-Based Employee Payroll: A Case Study on PT. Bio Pilar Utama,” Emerg. Inf. Sci. Technol., vol. 4, no. 2, Art. no. 2, Dec. 2023, doi: 10.18196/eist.v4i2.20732.
- Zaidan, A. A., AlSattar, H. A., Qahtan, S., Deveci, M., Pamucar, D., & Gupta, B. B. (2023). Secure decision approach for internet of healthcare things smart systems-based blockchain. IEEE Internet of Things Journal.
- Gupta, B. B., Gaurav, A., & Panigrahi, P. K. (2023). Analysis of security and privacy issues of information management of big data in B2B based healthcare systems. Journal of Business Research, 162, 113859.
Cite As
Ali S.R. (2024) PERSONALIZED MEDICINE: HOW AI IS TAILORING TREATMENTS, Insights2Techinfo, pp.1