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

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.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-02-28
DOI
https://doi.org/10.5281/zenodo.14937050
Primary Topic
Genomics and Rare Diseases
Type
article
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article

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

Zenodo (CERN European Organization for Nuclear Research)
Genomics and Rare Diseases
article

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

article en

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.

Zenodo (CERN European Organization for Nuclear Research)
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Genomics and Rare Diseases
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Optimizing Personalized Medicine: Investigating the Role of AI-Driven Genomic Analysis in Tailoring Treatment Plans for Patients with Rare Genetic Disorders · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS