Introduction: Artificial Intelligence (AI) has become a transformative force in medical biotechnology, enabling breakthroughs in diagnostics, drug development, and personalized medicine. By leveraging machine learning (ML) and deep learning (DL), AI systems can analyze vast datasets, identify patterns, and make predictions with unprecedented accuracy. This paper explores key methodologies, applications, and outcomes of AI in medical biotechnology.
Methods: This study reviews AI applications in medical biotechnology through:
• Diagnostic Tools: AI-based image recognition (e.g., radiology, pathology) and biomarker detection.
• Drug Discovery: Virtual screening, molecular docking simulations, and predictive toxicity modeling.
• Genomic Analysis: ML algorithms for variant calling, CRISPR guide RNA design, and disease risk prediction.
• Clinical Decision Support: Natural Language Processing (NLP) for electronic health record (EHR) analysis.
Data sources include PubMed, IEEE Xplore, and clinical trial databases (2015–2024).
Results: Key findings include:
• Diagnostics: AI models achieved >95% accuracy in detecting breast cancer from mammograms (e.g., Google’s DeepMind).
• Drug Development: AI reduced drug discovery timelines by 40% (e.g., Atomwise’s virtual screening).
• Genomics: ML predicted Alzheimer’s risk with 88% precision using genomic data (Nature, 2023).
• Challenges: Bias in training data (e.g., underrepresentation of ethnic groups) remains a critical issue.
Conclusion: AI is revolutionizing medical biotechnology but requires robust ethical frameworks and diverse datasets to mitigate biases. Future research should focus on explainable AI (XAI) and regulatory standardization.
Keywords: Artificial Intelligence (AI), Medical Biotechnology