Introduction: Artificial intelligence has rapidly penetrated the fields of medicine and biomedical science in recent decades. Its ability to process vast amounts of biological data, employ complex predictive algorithms, and perform multidimensional analyses of genetic information has made AI a vital tool in medical research. With the growing volume of genetic data and patient information, the need for intelligent and efficient methods to analyze such data has become increasingly apparent.
Historically, disease diagnosis and treatment design were largely based on clinical experience and limited datasets. With the advent of AI, it is now possible to analyze big data, identify hidden patterns, and predict treatment outcomes with greater accuracy. These advancements are particularly significant in hereditary diseases, cancer, and neurological disorders.
The aim of this paper is to provide a comprehensive review of AI applications in biomedical science, evaluate its impact on genetic mutation prediction, design of personalized therapies, and discuss associated ethical and scientific considerations.
Methods: This study employed a systematic literature review and computational analysis approach to investigate AI applications in biomedical science.
1. Data Collection:
Peer-reviewed articles were sourced from PubMed, Scopus, and Web of Science databases (2015–2025). Keywords included "Artificial Intelligence," "Machine Learning," "Genetic Mutation," "Personalized Medicine," and "Targeted Therapy."
2. Selection Criteria:
Studies included were original research, clinical trials, or computational studies relevant to AI applications in genomics, drug design, and personalized treatment. Review articles, meta-analyses, and case studies were included for supporting context.
3. Data Analysis:
Extracted data were categorized into three main areas: predictive modeling of genetic mutations, AI-driven drug design, and personalized treatment planning. Machine learning models (e.g., neural networks, random forest, support vector machines) were compared based on accuracy, predictive power, and applicability to clinical settings.
4. Ethical and Practical Assessment:
Challenges related to data privacy, model interpretability, and clinical implementation were analyzed to provide a comprehensive understanding of AI integration in biomedical practice.
Results: The review and analysis demonstrated several key findings:
1. Predicting Genetic Mutations:
AI models achieved prediction accuracies ranging from 85% to 95% in identifying pathogenic genetic mutations. Neural networks showed superior performance in complex genomic datasets compared to traditional statistical models.
2. Drug Design and Targeted Therapy:
Computational models guided by AI successfully predicted effective drug candidates with reduced side effects. Simulated drug screening reduced development time by approximately 30–40% in reviewed studies.
3. Personalized Medicine:
AI-driven approaches enabled tailored treatment plans based on patient-specific genetic and clinical profiles. Preliminary studies reported improved therapeutic outcomes, including higher response rates in oncology patients receiving AI-informed therapies.
4. Ethical and Implementation Considerations:
Key challenges included ensuring patient data confidentiality, avoiding algorithmic bias, and establishing regulatory standards for clinical adoption. Addressing these challenges is critical for safe and effective AI integration in healthcare.
Conclusion: Artificial intelligence, with its capacity to analyze large-scale data and provide precise predictions, has the potential to revolutionize biomedical science. From predicting genetic mutations to designing personalized therapies, AI serves as an effective tool for enhancing healthcare quality. Nonetheless, attention to ethical considerations, data protection, and close collaboration with healthcare professionals is crucial for successful implementation. The future of biomedical research is inconceivable without AI, which paves the way for precise and personalized medical care.