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Artificial Intelligence in Medical Research

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Female scientist and AI robot working together in the science lab

Artificial Intelligence (AI) in medical research offers numerous benefits, including increased diagnostic accuracy and the ability to analyze vast amounts of data quickly. AI can assist in predictive analytics, helping to forecast disease outbreaks and patient outcomes more effectively.

However, there are also challenges associated with AI in medical research. Data privacy and security are major concerns, as sensitive patient information must be protected from breaches. Additionally, there is a risk of algorithmic bias, which can lead to unequal treatment outcomes for different demographic groups. Ensuring the ethical use of AI and maintaining human oversight are crucial to addressing these challenges and maximizing the potential of AI in medical research.

We would like to invite you to read recently published articles on this topic to learn more.

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Highlight Articles

Endoscopy

"A computer-aided detection system in the everyday setting of diagnostic, screening, and surveillance colonoscopy" by Michiel H.J. Maas et al.

The aim of this study was to evaluate a computer-aided detection system in a varied colonoscopy population.

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Endoscopy

"Artificial intelligence (AI) systems for detection of Barrett’s neoplasia" by Albert Jeroen De Groof.

Endoscopic recognition of early Barrett’s neoplasia may be difficult; assistance for detection of Barrett’s neoplasia has therefore always been one of the most appealing AI applications.

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RöFo

"Smart scanning: automatic detection of superficially located lymph nodes using ultrasound" by Maximilian Rink et al.

The aim was to test whether already established programs for AI-assisted sonography of breast lesions and thyroid nodules are also suitable for identifying and measuring superficial lymph nodes.

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RöFo

"Structured reporting for efficient epidemiological and in-hospital prevalence analysis of pulmonary embolisms" by Tobias Jork et al.

In this study, a data mining algorithm was used to calculate epidemiological data and in-hospital prevalence statistics of pulmonary embolism (PE) by analyzing structured CT reports.

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Medical Knowledge meets AI

Thieme is one of the leading providers of medical specialist information. We spoke to Katrin Siems, Senior Executive Vice President Marketing and Sales at the Thieme Group, and AI expert Alexander Thamm about the opportunities and prospects of combining quality-assured specialist content and standardized patient data with artificial intelligence.

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Revolutionizing Patient Communication with AI

Thieme participates in the Viennese healthtech scale-up company XUND. Together, the companies want to support medical professionals with relevant information when making a diagnosis and choosing a therapy and improve communication with patients during this process. The basis for this will be XUND's AI-based technology and Thieme's high-quality specialist information. The aim is to combine both in a Medical Large Language Model (MedLLM) and thus enable the dynamic provision of personalized medical content.

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