Clinical Scorecard: Musings from The Power List: Flavio Antonio Franchina

At a Glance

Key Highlights

• Franchina says modern instruments produce more data, but detecting more does not necessarily mean knowing more.
• He describes harmonizing sample preparation, separation, quantification, and data processing into application-driven workflows for targeted and non-targeted environmental analysis and bioanalysis.
• He emphasizes separating signal from noise and determining whether detected compounds came from the sample, the environment, or unintended transformations during handling.
• He says detected features should be assessed for reliability, reproducibility, identification, and what the results can and cannot support.
• He expects generative and machine-learning tools to be integrated into analytical workflows and instrumentation, while expressing concern that scientists may run optimizations without understanding why they produce a given optimum.

Guideline-Based Recommendations

Diagnosis

Management

Monitoring & Follow-up

Risks

Patient & Prescribing Data

Not applicable; the article concerns analytical science rather than patient care.

No patient treatments or prescribing data are discussed.

Clinical Best Practices

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