Artificial Intelligence Applications to Improve the Treatment of Locally Advanced Non-Small Cell Lung Cancers.
Andrew HopeMaikel VerduinThomas J DillingAnanya ChoudhuryRianne R R FijtenLeonard WeeHugo J W L AertsIssam El NaqaRoss MitchellMarc VooijsAndre DekkerDirk de RuysscherAlberto TraversoPublished in: Cancers (2021)
Locally advanced non-small cell lung cancer patients represent around one third of newly diagnosed lung cancer patients. There remains a large unmet need to find treatment strategies that can improve the survival of these patients while minimizing therapeutical side effects. Increasing the availability of patients' data (imaging, electronic health records, patients' reported outcomes, and genomics) will enable the application of AI algorithms to improve therapy selections. In this review, we discuss how artificial intelligence (AI) can be integral to improving clinical decision support systems. To realize this, a roadmap for AI must be defined. We define six milestones involving a broad spectrum of stakeholders, from physicians to patients, that we feel are necessary for an optimal transition of AI into the clinic.
Keyphrases
- artificial intelligence
- newly diagnosed
- end stage renal disease
- machine learning
- ejection fraction
- chronic kidney disease
- electronic health record
- prognostic factors
- squamous cell carcinoma
- big data
- locally advanced
- clinical decision support
- stem cells
- type diabetes
- adipose tissue
- radiation therapy
- mass spectrometry
- lymph node
- open label
- smoking cessation