How Does AI Enable Personalized Imaging Protocols?

Author: Rasit Dinc

Medical imaging is a cornerstone of modern medicine, providing invaluable insights into the human body for diagnosis, treatment planning, and monitoring of disease. From X-rays to magnetic resonance imaging (MRI) and computed tomography (CT), these technologies have revolutionized healthcare. However, the traditional approach to imaging protocols has often been a one-size-fits-all model. This standardized approach, while ensuring consistency, can be suboptimal as it does not account for the vast anatomical and physiological diversity among patients. The advent of artificial intelligence (AI) is set to change this paradigm, ushering in an era of personalized imaging protocols tailored to the individual.

The Role of AI in Medical Imaging

Artificial intelligence, particularly machine learning and deep learning algorithms, is rapidly transforming the field of diagnostic imaging. AI excels at analyzing vast and complex datasets, identifying subtle patterns that may be imperceptible to the human eye [2]. This capability is being harnessed to improve the accuracy and efficiency of image interpretation, automate tedious tasks, and ultimately, enhance patient care [4]. By training on millions of medical images, AI models can learn to detect and classify abnormalities with remarkable precision, assisting radiologists in their diagnostic workflow.

AI-Powered Personalization of Imaging Protocols

The true power of AI in medical imaging lies in its ability to move beyond standardized procedures and enable personalized care. Here’s how AI is making personalized imaging protocols a reality:

Automated Protocol Optimization

AI algorithms can intelligently select and optimize imaging protocols based on a patient's unique characteristics, such as age, weight, gender, and clinical history [3]. For instance, an AI system could analyze a patient's electronic health record and suggest the most appropriate MRI sequence for their specific condition, ensuring that the resulting images have the highest diagnostic value. This automated optimization not only improves image quality but also reduces the need for manual adjustments by radiographers, streamlining the entire imaging process.

Dose Optimization

One of the most significant concerns in medical imaging, particularly with CT scans, is radiation exposure. AI is playing a crucial role in addressing this challenge by enabling personalized dose optimization. AI-driven systems can determine the lowest possible radiation dose for a given patient that will still produce a diagnostically acceptable image [1]. This is achieved by analyzing the patient's body mass index (BMI) and other factors to tailor the radiation exposure, minimizing the potential risks associated with radiation while maintaining diagnostic confidence.

Adaptive Scanning

The personalization of imaging protocols is not limited to the pre-scan phase. AI also enables adaptive scanning, where the imaging parameters are adjusted in real-time during the scan itself. For example, in an MRI scan, an AI algorithm can analyze the initial images as they are acquired and dynamically modify the scanning parameters to improve image quality or focus on a specific area of interest [14]. This real-time adaptation ensures that the final images are of the highest possible quality, reducing the need for repeat scans and improving the patient experience.

Predictive Modeling

Furthermore, AI can be used to develop predictive models that can forecast the optimal imaging protocol for a specific clinical question. By analyzing data from previous cases, AI can learn to predict which imaging protocol is most likely to provide the diagnostic information needed for a particular patient and condition. This predictive capability can help to improve diagnostic accuracy and ensure that patients receive the most appropriate and effective imaging examination from the outset.

Benefits of Personalized Imaging Protocols

The shift towards AI-enabled personalized imaging protocols offers a multitude of benefits for patients, clinicians, and healthcare systems as a whole. These include:

• Improved Diagnostic Accuracy: Tailoring imaging protocols to the individual can lead to higher quality images and more accurate diagnoses, enabling earlier detection of diseases like Alzheimer's [13].
• Reduced Radiation Exposure: Personalized dose optimization minimizes the risks associated with radiation, enhancing patient safety.
• Increased Efficiency: Automating protocol selection and optimization streamlines the radiology workflow, reducing scan times and increasing throughput.
• Enhanced Patient Experience: Faster scans and a reduced need for repeat examinations contribute to a more positive patient experience.

Challenges and Future Directions

Despite the immense potential of AI in personalized imaging, there are challenges to its widespread adoption. These include the need for large, high-quality datasets for training AI models, regulatory hurdles, and the seamless integration of AI tools into existing clinical workflows. However, the field is rapidly evolving, and ongoing research is focused on developing more sophisticated and robust AI algorithms. In the future, we can expect to see AI playing an even greater role in personalized medicine, with the integration of multi-modal data, such as genomics and proteomics, to create truly holistic and individualized imaging protocols.

Conclusion

Artificial intelligence is not just a futuristic concept; it is a powerful tool that is already reshaping the landscape of medical imaging. By enabling the personalization of imaging protocols, AI is helping to improve diagnostic accuracy, enhance patient safety, and optimize clinical workflows. As AI technology continues to mature, we can anticipate a new era of precision medicine, where every patient receives a truly personalized imaging experience, leading to better health outcomes for all.

References

[1] David-Olawade, A. C., et al. (2025). AI-Driven Advances in Low-Dose Imaging and Enhancement. PMC. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC11941271/

[2] Khalifa, M., et al. (2024). AI in diagnostic imaging: Revolutionising accuracy and... ScienceDirect. Retrieved from https://www.sciencedirect.com/science/article/pii/S2666990024000132

[3] AHRA. (2025). AI in Medical Imaging: Driving Efficiency and Innovation for Radiology Administrators. AHRA. Retrieved from https://link.ahra.org/Article/ai-in-medical-imaging-driving-efficiency-and-innovation-for-radiology-administrators

[4] RSNA. (2025). Role Of AI In Medical Imaging. RSNA. Retrieved from https://www.rsna.org/news/2025/january/role-of-ai-in-medical-imaging

[13] Huang, Y., et al. (2025). An adaptive learning framework for Alzheimer's disease... ScienceDirect. Retrieved from https://www.sciencedirect.com/science/article/pii/S2772662225001237

[14] Schloz, M., et al. (2023). Deep reinforcement learning for data-driven adaptive... Nature. Retrieved from https://www.nature.com/articles/s41598-023-35740-1