Literature DB >> 30709922

Artificial intelligence and the radiologist: the future in the Armed Forces Medical Services.

Debraj Sen1, R Chakrabarti2, S Chatterjee3, D S Grewal4, K Manrai4.   

Abstract

Artificial intelligence (AI) involves computational networks (neural networks) that simulate human intelligence. The incorporation of AI in radiology will help in dealing with the tedious, repetitive, time-consuming job of detecting relevant findings in diagnostic imaging and segmenting the detected images into smaller data. It would also help in identifying details that are oblivious to the human eye. AI will have an immense impact in populations with deficiency of radiologists and in screening programmes. By correlating imaging data from millions of patients and their clinico-demographic-therapy-morbidity-mortality profiles, AI could lead to identification of new imaging biomarkers. This would change therapy and direct new research. However, issues of standardisation, transparency, ethics, regulations, training, accreditation and safety are the challenges ahead. The Armed Forces Medical Services has widely dispersed units, medical echelons and roles ranging from small field units to large static tertiary care centres. They can incorporate AI-enabled radiological services to subserve small remotely located hospitals and detachments without posted radiologists and ease the load of radiologists in larger hospitals. Early widespread incorporation of information technology and enabled services in our hospitals, adequate funding, regular upgradation of software and hardware, dedicated trained manpower to manage the information technology services and train staff, and cyber security are issues that need to be addressed. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  Armed Forces Medical Services (AFMS); artificial intelligence (AI); deep learning; machine learning; radiology

Mesh:

Year:  2019        PMID: 30709922     DOI: 10.1136/jramc-2018-001055

Source DB:  PubMed          Journal:  BMJ Mil Health        ISSN: 2633-3767


  1 in total

1.  A deep learning approach for dental implant planning in cone-beam computed tomography images.

Authors:  Sevda Kurt Bayrakdar; Kaan Orhan; Ibrahim Sevki Bayrakdar; Elif Bilgir; Matvey Ezhov; Maxim Gusarev; Eugene Shumilov
Journal:  BMC Med Imaging       Date:  2021-05-19       Impact factor: 1.930

  1 in total

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