The past, present and future role of artificial intelligence in imaging. Issue 105 (August 2018)

• Record Type: Journal Article
• Authors: Fazal, Mohammad Ihsan; Patel, Muhammed Ebrahim; Tye, Jamie; Gupta, Yuri
• Is Part Of: European journal of radiology. Issue 105(2018)
• Page Start: 246
• Page End: 250
• Publication Date: 2018-08
• Subjects: Artificial intelligence -- Computer aided detection -- Computer aided diagnosis -- Technology -- Error rate; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2018.06.020
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: AI can act as a competent second reader of images and reduce error rate. AI is limited by a high false positive rate and an inability to display reasoning. Future avenues lie in use of patient records to generate diagnoses. Abstract: Artificial intelligence (AI) is already widely employed in various medical roles, and ongoing technological advances are encouraging more widespread use of AI in imaging. This is partly driven by the recognition of the significant frequency and clinical impact of human errors in radiology reporting, and the promise that AI can help improve the reliability as well the efficiency of imaging interpretation. AI in imaging was first envisioned in the 1960s, but initial attempts were limited by the technology of the day. It was the introduction of artificial neural networks and AI based computer aided detection (CAD) software in the 1980s that marked the advent of widespread integration of AI within radiology reporting. CAD is now routinely used in mammography, with consistent evidence of equivalent or improved lesion detection, with small increases in recall rates. Significant false positive rates remain a limitation for CAD, although these have markedly improved in the last decade. Other challenges include the difficulty clinicians encounter in trying to understand the reasoning of an AI system, which may limit their confidence in its advice, and a question mark hangs over who should be liable if CAD makes an error. The future integration of CAD with PACS promises the development of more comprehensively intelligent systems that can identify multiple, challenging diagnoses, and a move towards more individualised patient outcome predictions based upon AI analysis.