1. Introduction

2. Physics-Informed Machine Learning (PIML)

2.1. Biases as Taxonomy in PIML

3. Physics-Informed Medical Image Analysis (PIMIA)

3.1. Physics Incorporation in General ML and MIA

3.2. Intuitive Introduction to Physics Priors in MIA

3.3. Physics Prior Categories with Examples (Taxonomy I)

3.5. Typical Pipeline

4. PIMIA Tasks: Detailed Survey

4.1. Imaging

4.2. Image Generation

4.3. Inverse Imaging

4.5. Image Segmentation and Classification

4.6. Physics Informed Predictive Modeling

5. Quantitative Study and Insights

5.1. Calculation of Performance Improvement Score

5.2. Performance Improvement due to Physics Incorporation

5.3. Discussion: PIML Bias Based Taxonomy and PIMIA

5.4. When to Choose PIMIA Over Typical Data-Driven MIA?

6. Current Challenges and Future Research Direction

6.1. Challenges in Incorporating Physics into MIA

6.2. Future Directions

7. Conclusions