Artificial Intelligence

The EMS system supports real-time data transfer and two-way communication to empower clinical decision making on the move. The AI capabilities will provide “transformative change,” an exec says.

5G holds much promise for healthcare organizations to support innovative clinical use cases and use of technologies, as well as to help deliver critical data where it’s needed. But in order to unlock the power of 5G, healthcare organizations need to address four areas as they build their 5G roadmap.

The UK and the EU should 'work as one' to further the deployment of AI in healthcare, writes Dr Layla McCay, director of international relations at the NHS Confederation. 

A new study, conducted by Korean academic hospitals and Lunit, a medical AI company specializing in developing AI solutions for radiology and oncology, demonstrated the benefits of AI-aided breast cancer detection from mammography images. The study was published online on 6 February 2020, in Lancet Digital Health and features large-scale data of over 170,000 mammogram examinations from five institutions across South Korea, USA, and the UK, consisting of Asian and Caucasian female breast images. TOP FINDINGS One of the major findings showed that AI, in comparison to the radiologists, displayed better sensitivity in detecting cancer with mass (90% vs 78%) and distortion or asymmetry (90% vs 50%). The AI was better in the detection of T1 cancers, which is categorized as early-stage invasive cancer. AI detected 91% of T1 cancers and 87% of node-negative cancers, whereas the radiologist reader group detected 74% for both. Another finding was a significant improvement in the performance of radiologists, before and after using AI. According to the study, the AI alone showed 88.8% sensitivity in breast cancer detection, whereas radiologists alone showed 75.3%. When radiologists were aided by AI, the accuracy increased by 9.5% to 84.8%. An important factor in diagnosing mammograms is breast density and dense breast tissues, mostly from the Asian population, make it harder to interpret as dense tissue is more likely to mask cancers in mammograms. According to the study’s findings, the diagnostic performance of AI was less affected by breast density, whereas radiologists' performance was prone to density, showing higher sensitivity for fatty breasts at 79.2% compared to dense breasts at 73.8%. When aided by AI, the radiologists’ sensitivity when interpreting dense breasts increased by 11%. THE LARGER TREND Findings from a study published in Nature indicated that Google’s AI model spotted breast cancer in de-identified screening mammograms with greater accuracy, with fewer false positives and false negatives than experts, HealthCareITNews reported. Lunit recently raised a $26M Series C funding from Korean and Chinese investors, which the company said was its biggest funding round, according to a DealStreetAsia report in January.  ON THE RECORD “It is an unprecedented quantity of data with accurate ground truth--especially the 36,000 cancer cases, which is seven times larger than the usual number of datasets from resembling studies conducted previously,” said Hyo-Eun Kim, the first author of the study and Chief Product Officer at Lunit.  Prof. Eun-Kyung Kim, the corresponding author of the study and a breast radiologist at Yonsei University Severance Hospital, said: “One of the biggest problems in detecting malignant lesions from mammography images is that to reduce false negatives—missed cases—radiologists tend to increase recalls, casting a wider safety net, which brings an increased number of unnecessary biopsies.” “It requires extensive experience to correctly interpret breast images, and our study showed that AI can help find more breast cancer with lesser recalls, also detecting cancers in its early stage of development.” 

A senior vice president at the content services and enterprise imaging vendor offers an in-depth look at these three trends and advises conference attendees on what to do.

The general manager of IBM Watson Health talks digital transformation, cloud computing, the Internet of Things, blockchain, quantum computing and more.

Its new report on emerging technologies sees appetite and aptitude growing for telehealth and virtual visits, remote patient monitoring, clinical decision support and more.

The new module targets factors that influence hospital readmissions for diabetic patients and predicts the likelihood of future patient readmissions.

Its message to attendees will revolve around clinical documentation and how it is using AI, machine learning, NLP and more to ease physician workloads and enhance patient experiences.

We're looking for presentations with practical tips showing how hospitals and health systems have implemented machine learning and AI to improve care, control costs and drive operational efficiencies.