[Some reflections on evidenced-based medicine, precision medicine, and big data-based research]

Area of Science:

• Medical Practice
• Genetics
• Epidemiology

Background:

• Evidence-based medicine is crucial, but treatment decisions must balance evidence with resources and patient values.
• Many patients treated with common medications do not benefit, indicating imprecision in current diagnosis and treatment.
• Precision medicine seeks to identify individuals who will benefit from specific treatments, moving beyond a one-size-fits-all approach.

Purpose of the Study:

• To critically evaluate the concept and current approaches to precision medicine.
• To question the overreliance on gene sequencing and big data in precision medicine.
• To advocate for a more realistic, multi-factorial approach incorporating big data and genetics.

Main Methods:

• Literature review and critical analysis of existing medical paradigms.
• Examination of the limitations of gene sequencing and big data in medical research.
• Comparison of precision medicine's potential with traditional multi-factorial approaches.

Main Results:

• Most genes show weak associations with disease, limiting their utility for precise diagnosis and treatment.
• Big data's emphasis on correlation over causation and large sample sizes can be misleading and scientifically unsound.
• Randomized controlled trials remain essential for validating interventions, a role that big data analysis cannot replace.

Conclusions:

• Precision medicine is not a novel concept and should not solely depend on gene sequencing.
• The traditional multi-factorial approach, enhanced by big data and genetic factors, presents a more pragmatic future for precision medicine.
• Overemphasis on big data without scientific rigor risks futile interventions and undermines research validity.