Are estimations of radiomic image markers dispensable due to recent deep learning findings?

Current Applications of Artificial Intelligence in Sarcoidosis

PURPOSE

Sarcoidosis is a complex disease which can affect nearly every organ system with manifestations ranging from asymptomatic imaging findings to sudden cardiac death. As such, diagnosis and prognostication are topics of continued investigation. Recent technological advancements have introduced multiple modalities of artificial intelligence (AI) to the study of sarcoidosis. Machine learning, deep learning, and radiomics have predominantly been used to study sarcoidosis.

METHODS

Articles were collected by searching online databases using keywords such as sarcoid, machine learning, artificial intelligence, radiomics, and deep learning. Article titles and abstracts were reviewed for relevance by a single reviewer. Articles written in languages other than English were excluded.

CONCLUSIONS

Machine learning may be used to help diagnose pulmonary sarcoidosis and prognosticate in cardiac sarcoidosis. Deep learning is most comprehensively studied for diagnosis of pulmonary sarcoidosis and has less frequently been applied to prognostication in cardiac sarcoidosis. Radiomics has primarily been used to differentiate sarcoidosis from malignancy. To date, the use of AI in sarcoidosis is limited by the rarity of this disease, leading to small, suboptimal training sets. Nevertheless, there are applications of AI that have been used to study other systemic diseases, which may be adapted for use in sarcoidosis. These applications include discovery of new disease phenotypes, discovery of biomarkers of disease onset and activity, and treatment optimization.

Artificial intelligence in medical education and the meaning of interaction with natural intelligence - an interdisciplinary approach

Introduction: The practice of medicine is characterized by decision making in which digital techniques can provide good support. In this context, artificial intelligence (AI) is becoming increasingly important. The challenge for physicians, however, is to maintain an overview of the potential applications and usefulness of AI in order to be able to apply it efficiently and safely in their work. Therefore, appropriate skills must be imparted during the course of medical studies so that future practitioners can meet this requirement. Project description: The interdisciplinary research-related teaching and learning project "(Natural) Science and Technology in Medicine - NWTmed" brings together students at the Justus-Liebig-University Giessen (JLU) from the fields of medicine and other (natural) scientific disciplines in structured courses with the aim of thinking, learning, and working in an interdisciplinary and research-oriented manner already during their medical education. With the involvement of local researchers, a "multi-disciplinary" seminar on the basic premises, methods, and applications of AI was established. Results: The participants of the course came from a wide variety of fields of study, which promoted an interdisciplinary exchange and animated discussions. A gain in knowledge and an increase in interest in the topic of AI was noted in the evaluations, and a willingness on the part of the students to pursue further independent study was also expressed. Discussion and conclusion: The topic of AI and its relevance to the field of medicine is not yet sufficiently represented in medical education. It will require integration in the curriculum and performance evaluations as well as interdisciplinary and research-related teaching formats.