Table 1 Key elements for the future: opportunities, challenges and solutions of AI in infection and inflammation molecular imaging

Improve detection

Detection of low-grade or localized infections

Insufficient spatial resolution

Lack of sensitivity

Long acquisition times

Cardiac and respiratory motion correction

Improving of the detector sensitivity. e.g., by predicting the depth-of-interaction of incoming photons

Image denoising by AI

Predict outcomes

Prediction of individual outcome by assessing the systemic immune response

Validated data derived from multiple organ systems required

In depth analysis of high-dimensional imaging data by AI algorithms

Large-scale prospective trials including in vitro ‘omics’ data

Provide prognostic information

Imaging as predictive classifier to determine long-term outcome

Discrimination of physiological vs. pathological immune metabolic pathways

Subtle differences require large datasets for training

High efforts for data harmonization

AI analysis on big data provided by, e.g., large multicenter studies or national health care providers