Abstract
Background Large volumes of data increasing over time lead to a shortage of radiologistsʼ 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
Keywords diagnostic radiology - patient interaction - deep learning - artificial intelligence
- doctor patient relationship
