Abstract
At a glance
Artificial intelligence can support clinical work only when technical performance is matched by workflow fit, explainability, governance, and professional trust. This commentary outlines the practical questions that should guide responsible integration.
Author information
Authors: Michael Fischer, Thomas Schneider-Hoffmann, Hannah Vogt
Affiliation: Author affiliations available in the published article.
From model performance to clinical work
A model can perform well in evaluation and still fail to improve care. Clinical value depends on timing, data quality, responsibility, and the way advice fits existing decisions.
Integration should begin with a well-defined clinical need rather than with the availability of an algorithm.
Governance and professional trust
Clinicians need to understand when a system is reliable, when it may fail, and who remains accountable. Monitoring after deployment is as important as validation before it.
Patients also need meaningful explanations of how automated tools contribute to decisions that affect them.
Implementation as evidence
Responsible adoption requires prospective evaluation across different settings and patient populations. Workflow effects and unintended consequences should be measured directly.
The strongest systems will support professional judgement, reveal uncertainty, and improve care without hiding trade-offs.
References
The complete reference list remains available in the authoritative published PDF.
Open reference list in PDFHow to cite
Fischer, M., Schneider-Hoffmann, T., & Vogt, H. (2026). Clinical integration of artificial intelligence in healthcare. Science Academique, 7(1), 1–3.
