Developing an Artificial Intelligence–Enabled Health Care Practice: Rewiring Health Care Professions for Better Care

Abstract

Artificial intelligence (AI) has the potential to impact almost every aspect of health care, from detection to prediction and prevention. The adoption of new technologies in health care, however, lags far behind the emergence of new technologies. Health care professionals and organizations must be prepared to change and evolve to adopt these new technologies. A basic understanding of emerging AI technologies will be essential for all health care professionals. These technologies include expert systems, robotic process automation, natural language processing, machine learning, and deep learning. Health care professionals and organizations must build their capacity and capabilities to understand and appropriately adopt these technologies. This understanding starts with basic AI literacy, including data governance principles, basic statistics, data visualization, and the impact on clinical processes. Health care professionals and organizations will need to overcome several challenges and tackle core structural issues, such as access to data and the readiness of algorithms for clinical practice. However, health care professionals have an opportunity to shape the way that AI will be used and the outcomes that will be achieved. There is an urgent and emerging need for education and training so that appropriate technologies can be rapidly adopted, resulting in a healthier world for our patients and our communities.

Are Convolutional Neural Networks Trained on ImageNet Images Wearing Rose‐Colored Glasses?: A Quantitative Comparison of ImageNet, Computed Tomographic, Magnetic Resonance, Chest X‐Ray, and Point‐of‐Care Ultrasound Images for Quality

“…Deep learning algorithms should essentially act like super experts when it comes to image interpretation 14 . When properly trained and tested, they can rapidly and accurately recognize faces, objects, and anatomy and even pick out disease on head CT or MR imaging.…”

“…Deep learning algorithms should essentially act like super experts when it comes to image interpretation. 14 When properly trained and tested, they can rapidly and accurately recognize faces, objects, and anatomy and even pick out disease on head CT or MR imaging. In fact, CNNs started to outperform human experts at identifying everyday images correctly at international ImageNet competitions several years ago.…”

Indirect Recognition of Predefined Human Activities

“…Loukatos et al [ 3 ] investigated educationally fruitful speech-based methods to assist people with special needs to care for potted plants. Wiljer et al [ 4 ] suggested improving health care by developing an artificial intelligence-enabled healthcare practice. Many of the works in the field of activity recognition are emphasizing fall detections [ 5 , 6 , 7 ].…”

To the Lighthouse: Embracing a Grand Challenge for Cancer Education in the Digital Age

“…While they present clear options for success in multiple contexts, there is one simple common denominator for all organizations: there is a need to educate everyone-a certain level of AI literacy is essential at all levels of the organization [6]. In a recent article, we have argued that the creation of an AI-enabled health care organization requires building awareness and capacity, gaining experience in developing and/or implementing AI initiatives and innovations, establishing strategic partnerships, and mobilizing and exchanging rapidly emerging knowledge and science related to AI [7]. In addition, a certain level of AI literacy is required across the organization.…”

“…In addition, a certain level of AI literacy is required across the organization. The knowledge domains include (1) data governance and ethical principles, (2) basic statistics and the science of algorithms, (3) data visualization and data information exchange, and finally (4) an understanding of how AI will transform the processes related to care [7]. Cancer educators must not only develop these skills but also take a leading role in ensuring that organizations are prepared to implement AI technologies in an appropriate and responsible manner.…”