This insight was featured in the November 2019 issue of HealthCare Business News magazine. With over 150 independent software vendors developing machine learning solutions for medical imaging, sorting through the plethora of options to select vendors is a challenge. Here are 10 factors radiologists should consider (and questions they should ask) before partnering with vendors providing AI solutions for medical imaging. The foremost consideration for healthcare providers adopting AI into their clinical workflow is relevancy. Does the AI solution truly address the needs of the healthcare provider, regardless of the associated costs and inconveniences to implement such a solution?

Deep learning, also known as artificial intelligence, will increasingly be used in the interpretation of medical images to address many long-standing industry challenges. This will lead to a $300 million market by 2021, according to a new report by Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare information technology industry. In most countries, there are not enough radiologists to meet the ever-increasing demand for medical imaging. Consequently, many radiologists are working at full capacity. The situation will likely get worse, as imaging volumes are increasing at a faster rate than new radiologists entering the field.

Machine learning was undoubtedly one of the hottest topics in radiology last year, with a steady stream of academic research papers highlighting how machine learning, particularly deep learning, can outperform traditional algorithms or manual processes in certain use-cases. Investment in machine learning start-ups also continued, with several companies attracting early stage funding. To date, more than $100m has been invested in start-ups that are developing AI solutions for radiology. Furthermore, commercial activity gained pace, with at least 20 companies exhibiting AI-based products at the RSNA conference towards the end of the year, although most were prototypes and only a handful had regulatory clearance. Whilst the enthusiasm for machine learning is certainly justified, it inevitably raises expectations, potentially to unrealistic levels.

Computer-aided detection (CADe) systems are intended to identify a variety of cancers such as breast cancer, prostate cancer, and lung lesions. They are most commonly used to detect microcalcifications and masses on screening mammograms. Despite concerns regarding the benefits of CADe and the high rate of false positives and false negatives, the market has grown steadily over the last two decades, most notably in the US where more than 90% of mammograms are interpreted using CADe. This has largely been driven by the availability of reimbursement for the use of CADe in breast screening. It is far less commonly used for detecting other cancers, where reimbursement for using CADe is currently not available.

Artificial intelligence, such as neural networks, deep learning and predictive analytics, has the potential to transform radiology, by enhancing the productivity of radiologists and helping them to make better diagnoses. This short report from Signify Research presents 5 reasons why artificial intelligence will increasingly be used in radiology in the coming years and concludes with a list of the barriers that will first need to be overcome before mainstream adoption will occur.