In a recent review published in the journal of Nature Medicine, scientists discussed the results of a two-year weekly effort to track and communicate significant developments in medical (artificial intelligence) AI. They included prospective studies as well as developments in medical image analysis that have narrowed the gap between research and implementation. They also discuss non-image data sources, innovative issue formulations, and human-AI collaboration as prospective pathways for novel medical AI research. As the medical AI community navigates the many ethical, technical, and human-centered issues required for safe and successful translation, the deployment of medical AI systems in routine clinical care presents an important but largely unrealized opportunity. Many randomized controlled trials (RCTs) have been used to assess the utility of AI systems in healthcare.
Industry leaders anticipate that the use of artificial intelligence in medical imaging will have a substantial clinical impact, ushering in an opportunity to significantly improve decision support in medical image interpretation. In this post, we cover a variety of promising medical imaging applications for AI and machine learning--including diagnosing cancer and brain aneurysms--as well as recent regulatory developments. CB Insights reports that healthcare-related AI investment totaled $1.44 billion in the first half of 2019, putting investment in the space on track to surpass the prior year, in which investment reached $2.5 billion. Much of the attention to date has surrounded applications in medical imaging or radiology. The National Center for Biologic Information (NCBI) reports that publications covering AI in radiology have steeply increased in recent years.
Lekadir, Karim, Osuala, Richard, Gallin, Catherine, Lazrak, Noussair, Kushibar, Kaisar, Tsakou, Gianna, Aussó, Susanna, Alberich, Leonor Cerdá, Marias, Konstantinos, Tskinakis, Manolis, Colantonio, Sara, Papanikolaou, Nickolas, Salahuddin, Zohaib, Woodruff, Henry C, Lambin, Philippe, Martí-Bonmatí, Luis
The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Despite these concerns and risks, there are currently no concrete guidelines and best practices for guiding future AI developments in medical imaging towards increased trust, safety and adoption. To bridge this gap, this paper introduces a careful selection of guiding principles drawn from the accumulated experiences, consensus, and best practices from five large European projects on AI in Health Imaging. These guiding principles are named FUTURE-AI and its building blocks consist of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability. In a step-by-step approach, these guidelines are further translated into a framework of concrete recommendations for specifying, developing, evaluating, and deploying technically, clinically and ethically trustworthy AI solutions into clinical practice.
CHICAGO - Scientists have developed an algorithm that predicts potentially dangerous low blood pressure, or hypotension, that can occur during surgery. The algorithm identifies hypotension 15 minutes before it occurs in 84 percent of cases, the researchers report in a new study published in the Online First edition of Anesthesiology, the peer-reviewed medical journal of the American Society of Anesthesiologists (ASA). A variety of factors can impact blood pressure during surgery. In some people, these factors may cause a significant drop in blood pressure. "Physicians haven't had a way to predict hypotension during surgery, so they have to be reactive, and treat it immediately without any prior warning.
Artificial intelligence--the mimicking of human cognition by computers--was once a fable in science fiction but is becoming reality in medicine. The combination of big data and artificial intelligence, referred to by some as the fourth industrial revolution,1 will change radiology and pathology along with other medical specialties. Although reports of radiologists and pathologists being replaced by computers seem exaggerated,2 these specialties must plan strategically for a future in which artificial intelligence is part of the health care workforce. Radiologists have always revered machines and technology. In 1960, Lusted predicted "an electronic scanner-computer to examine chest photofluorograms, to separate the clearly normal chest films from the abnormal chest films."3