Haemodynamic profiling: when AI tells us what we already know.

Area of Science:

• Clinical informatics
• Biomedical data science
• Artificial intelligence in medicine

Background:

• Machine learning (ML) algorithms offer advanced capabilities for processing large clinical datasets.
• The potential of ML lies in extracting complex patterns beyond human analytical capacity.
• However, the practical utility of ML in clinical settings requires careful evaluation.

Discussion:

• ML algorithms may not consistently yield novel or actionable clinical insights.
• In some cases, ML outputs reiterate information already apparent to clinicians.
• The application of ML to 'small data' scenarios, such as limited hemodynamic variables, is questionable.

Key Insights:

• The effectiveness of ML in generating actionable clinical insights from big data is not guaranteed.
• ML may be redundant for analyzing small datasets where traditional methods suffice.
• The added value of ML-driven hemodynamic profiling over conventional tools is uncertain.

Outlook:

• Further research is needed to determine the specific clinical contexts where ML provides significant advantages.
• Comparative studies are essential to assess ML's performance against established decision support systems.
• Defining the optimal role of ML in clinical data analysis remains an ongoing challenge.