Leveraging Artificial Intelligence and Machine Learning to Optimize Scientific and Clinical Data Processing

The rapid integration of artificial intelligence and machine learning into scientific research marks a transformative shift in contemporary methodology. From optimizing high-dimensional data processing in genomics and proteomics to automating clinical trial recruitment and target discovery, these computational architectures offer unprecedented opportunities for accelerating knowledge discovery. However, the reliance on generative models introduces significant risks, including scientific misinformation, algorithmic hallucinations, and challenges to reproducibility. Recent surveys highlight a critical tension: while the majority of researchers anticipate substantial gains in operational efficiency and work quality, they express deep concern regarding epistemic reliability and the potential erosion of critical thinking. This article provides a comprehensive overview of the current landscape of artificial intelligence in research, emphasizing the imperative for Explainable Artificial Intelligence and verifiable data frameworks. We argue that the future of scientific advancement necessitates a robust human-in-the-loop approach, ensuring that technological progress remains strictly aligned with the rigorous standards of peer-reviewed validation. By prioritizing transparency and methodological oversight, the scientific community can harness the power of machine learning while mitigating the risks to evidence-based discovery.

کلیدواژه ها:

Artificial Intelligence ، Machine Learning ، GenAI ، Explainable AI