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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Articles linked to this work by shared authors, journal, and citation graph.

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Retraction Note: Telomerase gene therapy: a remission toward cancer.

Medical oncology (Northwood, London, England)·2026
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RETRACTED: Quazi, S.; Malik, J.A. A Systematic Review of Personalized Health Applications through Human-Computer Interactions (HCI) on Cardiovascular Health Optimization. <i>J. Cardiovasc. Dev. Dis.</i> 2022, <i>9</i>, 273.

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RETRACTED: Quazi et al. The Discovery of Novel Antimicrobial Agents Through the Application of Isocyanide-Based Multicomponent Reactions. <i>Antibiotics</i> 2023, <i>12</i>, 849.

Antibiotics (Basel, Switzerland)·2025
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RETRACTED: Gupta et al. Chickpea Peptide: A Nutraceutical Molecule Corroborating Neurodegenerative and ACE-I Inhibition. <i>Nutrients</i> 2022, <i>14</i>, 4824.

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RETRACTED: Sibuh et al. The Emerging Role of Epigenetics in Metabolism and Endocrinology. <i>Biology</i> 2023, <i>12</i>, 256.

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Retraction Note: Artificial intelligence and machine learning in precision and genomic medicine.

Medical oncology (Northwood, London, England)·2025

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Artificial intelligence and machine learning in precision and genomic medicine.

Sameer Quazi1,2

  • 1GenLab Biosolutions Private Limited, Bangalore, Karnataka, 560043, India. colonel.quazi@gmail.com.

Medical Oncology (Northwood, London, England)
|June 15, 2022
PubMed
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Precision medicine and artificial intelligence (AI) enhance healthcare by enabling early disease risk prediction and personalized treatments. Machine learning algorithms are key to analyzing complex genomic data for improved patient outcomes.

Keywords:
Artificial IntelligenceGenomic MedicineMachine LearningPrecision MedicineTherapeutic

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Area of Science:

  • Genomic Medicine
  • Precision Medicine
  • Artificial Intelligence

Background:

  • Conventional treatments focus on symptoms, while precision medicine enables early risk prediction and personalized therapies.
  • Integrating patient data and broad factors is crucial for distinguishing between healthy and ill individuals.
  • Genomic medicine and AI offer transformative potential for patient care, especially for those with unique needs.

Purpose of the Study:

  • To review the contributions of machine learning (ML) algorithms in precision and genomic medicine.
  • To highlight how AI enhances physician decision-making through advanced computation and learning.
  • To underscore the role of high-throughput cell characteristic measurements in predictive modeling.

Main Methods:

  • Review of existing literature on ML applications in precision and genomic medicine.
  • Analysis of how AI facilitates reasoning and learning for enhanced clinical decision support.
  • Examination of high-throughput data, including gene expression and protein binding, as training objectives.

Main Results:

  • ML algorithms are pivotal in analyzing complex datasets for precision and genomic medicine.
  • AI provides computational insights, improving diagnostic accuracy and treatment customization.
  • The integration of diverse datasets and ML techniques heralds a new era in genomic medicine.

Conclusions:

  • Machine learning significantly contributes to the advancement of precision and genomic medicine.
  • AI-powered insights enhance the effectiveness of personalized healthcare strategies.
  • The synergy between genomic data, AI, and ML promises more effective and individualized patient care.