Towards Machine Learning-Aided Lung Cancer Clinical Routines: Approaches and Open Challenges

• Type: Article in International Scientific Journal
• Date: 2022

Authors

Silva, F (Author) Other; Pereira, T (Author) Other; Neves, I (Author) Other; Morgado, J (Author) Other; Freitas, C (Author) Other; Malafaia, M (Author) Other; Sousa, J (Author) Other; Fonseca, J (Author) Other; Negrao, E (Author) Other; de Lima, BF (Author) Other; da Silva, MC (Author) Other; António Madureira (Author) FMUP; Ramos, I (Author) Other; Costa, JL (Author) Other; Hespanhol V (Author) FMUP; Cunha, A (Author) Other; Oliveira, HP (Author) FCUP

Journal

The Journal is awaiting validation by the Administrative Services.

Title: JOURNAL OF PERSONALIZED MEDICINE

Vol. 32

Final page: 480

ISSN: 2075-4426

Publisher: MDPI

Indexing

ISI Web of Knowledge - 17 Citations

Scopus - 26 Citations

Other information

Authenticus ID: P-00W-9B8

DOI: 10.3390/jpm12030480

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 36