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What Will Analytical Science Look Like in 2036? Part One: The AI Age
Members of The Analytical Scientist’s 2026 Top 40 Under 40 consider how AI could reshape data interpretation, method development, instrumentation, and the work of analytical scientists – while warning against sacrificing understanding, rigor, or curiosity
Clinical Scorecard: What Will Analytical Science Look Like in 2036? Part One: The AI Age
At a Glance
Category
Detail
Condition
Artificial intelligence in analytical science
Key Mechanisms
Contributors describe AI applications in complex-data interpretation, predictive modeling, experimental planning, instrument development and operation, and closed-loop laboratory workflows.
Target Population
Analytical scientists and researchers working with analytical technologies, including mass spectrometry and multi-omics.
Care Setting
Research and technical-development laboratories; contributors discuss current and anticipated uses of AI, not clinical care.
Key Highlights
Contributors anticipate broader integration of AI across analytical workflows, from instrument development and experimental design to data interpretation and reporting.
Contributors describe AI-assisted interpretation of complex analytical datasets and the potential to identify unusual or overlooked features.
Autonomous laboratories can use experimental results to inform subsequent experiments in closed-loop workflows.
One contributor describes structured knowledge of prior experiments—including what worked or failed, conditions, and analytical purpose—as a way to support more reproducible, transferable, and efficient method development.
Contributors emphasize researchers’ expertise and judgment in designing robust experiments and evaluating AI-generated insights.
Guideline-Based Recommendations
Diagnosis
Management
Monitoring & Follow-up
Risks
Patient & Prescribing Data
Not applicable; the article discusses analytical science and does not provide patient or prescribing data.
No treatment recommendations or prescribing information are provided.
Clinical Best Practices
Henry Day says analytical scientists will need to focus on designing robust experiments, asking the right questions, and applying scientific judgment to interpret and challenge AI-generated insights.
Bob Pirok describes capturing and structuring knowledge from prior experiments, including what worked or failed, conditions, and analytical purpose.
Christopher P. Rüger hopes AI will expand researchers’ curiosity rather than replace it.
