The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review.

Autor:	Stueckle CA; Clinical Radiology, University Witten Herdecke Faculty of Health, Witten, Germany., Haage P; Diagnostic and Interventional Radiology, HELIOS Universitätsklinikum Wuppertal, Germany.
Jazyk:	English; German
Zdroj:	RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin [Rofo] 2024 Nov; Vol. 196 (11), pp. 1115-1124. Date of Electronic Publication: 2024 Apr 03.
DOI:	10.1055/a-2271-0799
Abstrakt:	Background: Large volumes of data increasing over time lead to a shortage of radiologists' time. The use of systems based on artificial intelligence (AI) offers opportunities to relieve the burden on radiologists. The AI systems are usually optimized for a radiological area. Radiologists must understand the basic features of its technical function in order to be able to assess the weaknesses and possible errors of the system and use the strengths of the system. This "explainability" creates trust in an AI system and shows its limits. Method: Based on an expanded Medline search for the key words "radiology, artificial intelligence, referring physician interaction, patient interaction, job satisfaction, communication of findings, expectations", subjective additional relevant articles were considered for this narrative review. Results: The use of AI is well advanced, especially in radiology. The programmer should provide the radiologist with clear explanations as to how the system works. All systems on the market have strengths and weaknesses. Some of the optimizations are unintentionally specific, as they are often adapted too precisely to a certain environment that often does not exist in practice - this is known as "overfitting". It should also be noted that there are specific weak points in the systems, so-called "adversarial examples", which lead to fatal misdiagnoses by the AI even though these cannot be visually distinguished from an unremarkable finding by the radiologist. The user must know which diseases the system is trained for, which organ systems are recognized and taken into account by the AI, and, accordingly, which are not properly assessed. This means that the user can and must critically review the results and adjust the findings if necessary. Correctly applied AI can result in a time savings for the radiologist. If he knows how the system works, he only has to spend a short amount of time checking the results. The time saved can be used for communication with patients and referring physicians and thus contribute to higher job satisfaction. Conclusion: Radiology is a constantly evolving specialty with enormous responsibility, as radiologists often make the diagnosis to be treated. AI-supported systems should be used consistently to provide relief and support. Radiologists need to know the strengths, weaknesses, and areas of application of these AI systems in order to save time. The time gained can be used for communication with patients and referring physicians. Key Points: · Explainable AI systems help to improve workflow and to save time.. · The physician must critically review AI results, under consideration of the limitations of the AI.. · The AI system will only provide useful results if it has been adapted to the data type and data origin.. · The communicating radiologist interested in the patient is important for the visibility of the discipline.. Citation Format: · Stueckle CA, Haage P. The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review. Fortschr Röntgenstr 2024; 196: 1115 - 1123. Competing Interests: The authors declare that they have no conflict of interest. (Thieme. All rights reserved.)
Databáze:	MEDLINE
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