Optimizing Personalized Medicine: Investigating the Role of AI-Driven Genomic Analysis in Tailoring Treatment Plans for Patients with Rare Genetic Disorders


Authors : Rishi Reddy Kothinti

Volume/Issue : Volume 10 - 2025, Issue 2 - February

Google Scholar : https://tinyurl.com/4setn5nb

Scribd : https://tinyurl.com/yepscd8v

DOI : https://doi.org/10.5281/zenodo.14937050

Abstract : The abstract introduces the era of personalized medicine, which tailors treatment options according to individual genetic, environmental, and lifestyle factors. AI-driven genomic analysis's contribution is further augmented by machine learning and computational biology in handling enormous genomic data, identifying genetic mutations, and predicting treatment responses. This paper emphasizes the need for the optimization provided by AI for personalized treatment planning, especially concerning patients with rare genetic disorders. Rare genetic diseases usually include simple gene mutations. They pose a challenge in terms of diagnosis and treatment because of a scarcity of information on the disease, high costs, and the absence of targeted therapies that address these conditions. Genetic disorders usually present an acute challenge to diagnostics as these are inefficient and often delay treatment. AI in genomic analysis is a tool that aids in expediting disease identification and improves drug investigation and gene therapy design. For AI, this means going beyond just finding genetic variants that determine drug responses toward effective interventions. This paper highlights AI applications for rare genetic disorder diagnostics, CRISPR gene-editing optimization, and applications in precision oncology. IBM Watson for Oncology is an AI-assisted platform that reinforces decision-making in treatment-by-design approaches. In a nutshell, integrating AI into personalized medicine would provide opportunities for healthcare workers to rectify a misdiagnosis, speed up treatment commencement, and improve the quality of life for patients. Findings support the need for AI-driven genomic analysis to improve traditional practice's limitations. AI implementing precision medicine presents avenues to better and more available therapies, thus enhancing the quality of life of those with rare genetic disorders.

Keywords : Personalized Medicine, AI-Driven Genomic Analysis, Rare Genetic Disorders, Precision Medicine, Genomic-Based Treatment Planni.

References :

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The abstract introduces the era of personalized medicine, which tailors treatment options according to individual genetic, environmental, and lifestyle factors. AI-driven genomic analysis's contribution is further augmented by machine learning and computational biology in handling enormous genomic data, identifying genetic mutations, and predicting treatment responses. This paper emphasizes the need for the optimization provided by AI for personalized treatment planning, especially concerning patients with rare genetic disorders. Rare genetic diseases usually include simple gene mutations. They pose a challenge in terms of diagnosis and treatment because of a scarcity of information on the disease, high costs, and the absence of targeted therapies that address these conditions. Genetic disorders usually present an acute challenge to diagnostics as these are inefficient and often delay treatment. AI in genomic analysis is a tool that aids in expediting disease identification and improves drug investigation and gene therapy design. For AI, this means going beyond just finding genetic variants that determine drug responses toward effective interventions. This paper highlights AI applications for rare genetic disorder diagnostics, CRISPR gene-editing optimization, and applications in precision oncology. IBM Watson for Oncology is an AI-assisted platform that reinforces decision-making in treatment-by-design approaches. In a nutshell, integrating AI into personalized medicine would provide opportunities for healthcare workers to rectify a misdiagnosis, speed up treatment commencement, and improve the quality of life for patients. Findings support the need for AI-driven genomic analysis to improve traditional practice's limitations. AI implementing precision medicine presents avenues to better and more available therapies, thus enhancing the quality of life of those with rare genetic disorders.

Keywords : Personalized Medicine, AI-Driven Genomic Analysis, Rare Genetic Disorders, Precision Medicine, Genomic-Based Treatment Planni.

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