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With the help of artificial intelligence, computers are to simulate human thought processes. Machine learning is intended to support almost all medical specialties. But what is going on inside an AI algorithm, what are its decisions based on? Can you even entrust a medical diagnosis to a machine? Clarifying these questions remains a central aspect of AI research and development.

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Article • Radiology & artificial intelligence

How to integrate AI in the clinical workflow: 7 lessons

In radiology, it is not about if but about when artificial intelligence (AI) will be used, said Professor Dr Tim Leiner of Utrecht University Medical Center at this year’s European Congress of…

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Article • The role of the radiographer

AI in radiation protection: a potential game changer

Radiographers could help design new artificial intelligence (AI) tools for radiation protection, Mark McEntee, professor of diagnostic radiography at University College Cork, Ireland, argued during…

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Article • Assistance and decision-making systems

Artificial intelligence for preoperative planning

In surgery, artificial intelligence (AI) is applied mostly in imaging, navigation, and robotic intervention. However, AI can also play a major role in preoperative planning. Objective…

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Article • AI-based personalized medical care

I³lung: EU launches lung cancer initiative

This summer, The European Commission launched I3lung, a new research initiative as a part of Horizon Europe, the EU’s research and innovation program. This research initiative aims to create a…

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Article • Regulatory challenges for AI-based diagnostics

Further IVDR changes: a step in the right direction, but…

New changes made to the timetable for the In vitro Diagnostic Medical Device Regulation (IVDR) across Europe could have a significant impact on manufacturers and users, an expert points out. While…

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Article • Visceral imaging

Endosonography: AI takes on the “supreme discipline”

Endosonography poses unique challenges for medical professionals, because two demanding disciplines have to be mastered at the same time. The use of artificial intelligence (AI) could help speed up…

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Article • Targeted Real-time Early Warning System for hospitals

Early detection of sepsis with the help of AI

Sepsis, a life-threatening, systemic, toxic bodily reaction to infection, is often difficult to detect in its early stages. Its symptoms, including fever, shortness of breath, rapid heart rate, and confusion, are associated with many medical conditions of hospitalized patients. But if not treated rapidly, a patient may die. The Targeted Real-time Early Warning System (TREWS) for sepsis detection…

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Article • Diagnostic assistant systems

AI in endoscopy: helper, trainer – influencer?

Artificial intelligence (AI) is increasing its foothold in endoscopy. Although the algorithms often detect pathologies faster than humans, their use also generates new problems. PD Dr Alexander Hann attests to the great potential of AI helpers. However, the deputy head of gastroenterology at the University Hospital Würzburg points out that their use can affect not only the reporting of findings…

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Supervised learning approach

A new deep learning-based algorithm to predict relapse-free survival in papillary thyroid carcinoma

The tall cell variant (TCV) is an aggressive subtype of papillary thyroid carcinoma (PTC). Sebastian Stenman, researcher from the Institute for Molecular Medicine, and the Department of Pathology at the University of Helsinki, Finland, is developing and training a deep learning algorithm using supervised learning to detect and quantify the proportion of tall cells in PTC.

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Article • AI provides prognostic information

Next-generation deep learning models predict cancer survival

Deaths from cancer are currently estimated at 10 million each year worldwide. Conventional cancer staging systems aim to categorize patients into different groups with distinct outcomes. ‘However, even within a specific stage, there is often substantial variation in patient outcomes,’ Markus Plass, academic researcher from the Medical University of Graz, Austria, explained to Healthcare in…

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Malignant tumour management

’Our machine learning model achieved 88.9% accuracy in predicting the sarcoma-specific survival rate’

Clinical management of soft tissue sarcoma is particularly challenging. Dr Sebastian Foersch, researcher at the Institute of Pathology at the University Medical Center in Mainz, Germany, has used a deep learning model for diagnosis and prognosis prediction of soft tissue sarcoma using conventional histopathology slides.

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Article • Possibilities and risks

AI in cardiology: so much is feasible – but is everything useful?

It might sound like science fiction but it is reality in cardiology: with the help of artificial intelligence (AI) physicians can recognize from a patient’s headshot whether the person is suffering from coronary artery disease and is therefore at risk of myocardial infarction. But is that knowledge really useful? Professor Dr David Duncker calls for a differentiated and careful assessment of…

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