Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges
Authors:
- Francisco SilvaFSFrancisco Silva
- Tania PereiraTania Pereira
- Inês NevesINInês Neves
- Joana MorgadoJMJoana Morgado
- Cláudia FreitasCFCláudia Freitas
- Mafalda MalafaiaMMMafalda Malafaia
- Joana SousaJSJoana Sousa
- João FonsecaJFJoão Fonseca
- Eduardo NegrãoENEduardo Negrão
- Beatriz Flor de LimaBLBeatriz Flor de Lima
- Miguel Correia da SilvaMSMiguel Correia da Silva
- António J. MadureiraAMAntónio J. Madureira
Open Access
- 16 March 2022
- journal article
- research article
- Published by MDPI AG in Journal of Personalized Medicine
- Vol. 12 (3) , 480
Abstract
Advancements in the development of computer-aided decision (CAD) systems for clinical routines provide unquestionable benefits in connecting human medical expertise with machine intelligence, to achieve better quality healthcare. Considering the large number of incidences and mortality numbers associated with lung cancer, there is a need for the most accurate clinical procedures; thus, the possibility of using artificial intelligence (AI) tools for decision support is becoming a closer reality. At any stage of the lung cancer clinical pathway, specific obstacles are identified and “motivate” the application of innovative AI solutions. This work provides a comprehensive review of the most recent research dedicated toward the development of CAD tools using computed tomography images for lung cancer-related tasks. We discuss the major challenges and provide critical perspectives on future directions. Although we focus on lung cancer in this review, we also provide a more clear definition of the path used to integrate AI in healthcare, emphasizing fundamental research points that are crucial for overcoming current barriers.Keywords
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