Data Science in Evidence-Based Medicine
Radenkovic et al. (2019) explore how data science revolutionizes evidence-based medicine, highlighting its role in enhancing accuracy in clinical practices and patient care. The integration of data analytics enables healthcare leaders to innovate and adopt data-driven strategies for better health outcomes (Ellis, 2024). As the demand for healthcare data scientists grows, understanding job qualifications and skills required is essential (Meyer, 2019). Recent advancements in large language models, like AlphaCode and CodeRAG-Bench, showcase their potential in automating code generation for healthcare applications, streamlining processes from clinical trial design to genomic data analysis (Zhang et al., 2023; Tayebi Arasteh et al., 2024). Furthermore, platforms like cBioPortal and tools developed by OpenAI exemplify the convergence of AI and healthcare, offering resources for researchers and practitioners (Grattafiori et al., 2024). Continuing to harness data science is crucial for advancing evidence-based practices in modern medicine.
