Perspectives of Evidence-Based Therapy Management

Area of Science:

• Radiology and Medical Imaging
• Artificial Intelligence in Healthcare
• Precision Medicine

Background:

• Current therapeutics necessitate precise patient stratification and disease subclassification.
• Anatomical imaging alone is insufficient for detailed disease description and therapy monitoring.
• There is a critical need to visualize molecular and pathophysiological processes for effective treatment.

Purpose of the Study:

• To review molecular imaging and AI-based diagnostic strategies for evidence-based therapy management.
• To present examples and clinical successes of these advanced diagnostic approaches.
• To discuss methods for generating structured and anonymized datasets for AI applications.

Main Methods:

• Molecular imaging to detect key pathological biomarkers.
• AI-based analysis of multiple imaging markers and clinical data for pattern recognition.
• Utilizing Generative Adversarial Networks for synthetic data generation to augment databases.

Main Results:

• Molecular imaging and radiomics offer complementary disease biomarkers.
• Data-driven and model-based diagnostics enhance precision medicine.
• AI-assisted multiparametric analyses require structured and large datasets, with synthetic data generation as a key enabler.

Conclusions:

• Molecular imaging and computer-assisted data analysis are complementary for characterizing pathophysiological processes.
• Both methods hold significant potential to individually or jointly improve future evidence-based therapy management.
• These approaches are pivotal for advancing personalized treatment strategies.