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[Data-driven integrated diagnostics: the natural evolution of clinical chemistry?]
Wouter W van Solinge1,2, Maarten J Ten Berg1, Saskia Haitjema1
1UMC Utrecht, Laboratorium Klinische Chemie en Haematologie, Utrecht.
Nederlands Tijdschrift Voor Geneeskunde
|March 16, 2019
Summary
Artificial intelligence will enhance medical diagnosis. Clinical chemists are well-positioned to lead the development of AI-driven diagnostic algorithms using big data for improved healthcare value.
Area of Science:
- Medical Informatics
- Artificial Intelligence in Healthcare
- Clinical Chemistry
Background:
- Medical diagnosis is increasingly reliant on artificial intelligence (AI).
- Developing AI diagnostic tools requires integrating diverse individual data.
- A multidisciplinary approach involving healthcare professionals, patients, and data scientists is essential.
Purpose of the Study:
- To highlight the role of clinical chemists in advancing AI-driven medical diagnosis.
- To emphasize the potential of self-learning diagnostic algorithms.
- To explore leveraging big data for enhanced healthcare value.
Main Methods:
- Conceptual framework outlining the integration of AI in diagnostics.
- Discussion on the multidisciplinary collaboration needed.
- Analysis of the clinical chemist's strategic position.
Main Results:
- AI is poised to significantly support accurate medical diagnoses.
- Clinical chemists are ideally positioned to lead AI algorithm development.
- Integrating comprehensive individual data is key for effective AI diagnostics.
Conclusions:
- The clinical chemist's expertise is crucial for developing and implementing self-learning diagnostic algorithms.
- Harnessing big data through AI can create significant value in healthcare.
- A collaborative, multidisciplinary effort is vital for the future of AI in medical diagnosis.
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