The artificial intelligence advantage: Supercharging exploratory data analysis.

Area of Science:

• Data Science
• Artificial Intelligence
• Machine Learning

Background:

• Explorative Data Analysis (EDA) is essential for scientific discovery, traditionally relying on manual methods.
• Artificial Intelligence (AI) and Machine Learning (ML) offer advanced computational approaches to enhance EDA.
• This review focuses on AI/ML's role in improving feature engineering and selection within EDA.

Discussion:

• AI and ML algorithms, including tree-based models, regularized regression, and clustering, automate feature importance and selection.
• These methods effectively handle complex data interactions, identify hidden patterns, and detect anomalies.
• Applications range from risk prediction in total hip arthroplasty to patient subgroup identification in scoliosis.

Key Insights:

• AI/ML integration accelerates EDA tasks and uncovers deeper data insights.
• Key techniques include automated feature ranking, interaction handling, and anomaly detection.
• Successful implementation requires understanding algorithms, their limitations, and domain-specific knowledge.

Outlook:

• Advancements in explainable AI and automated EDA promise further improvements.
• AI's role in EDA will grow, augmenting human expertise for better decision-making.
• The synergy of AI and human insight is crucial for navigating increasing data volumes across scientific domains.