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The rapid advancement in artifical intelligence (AI) and machine learning (ML) have led to significant advantages in automation within the medical imaging field, particularly in tasks such as image analysis, segmentation and report generation. However, a common concern amongst healthcare workers is the fear that AI will replace them. It’s important to note that both AI should be used a tool to assist an individual with a task. The aim of this article is to examine the benefits AI and ML has already provided to the medical imaging community.
AI Driven Automation
In both the clinical and research setting, there are many repetitive tasks that are required of a medical imager. AI excels at automating repetitive tasks and time-consuming tasks, allowing professionals to focus on more complex tasks. For example, AI systems like Philips’ Radiology Smart Assistant and Siemens’ myExam Companion improve workflow efficiency by providing real time feedback on optimising patient positioning, reducing radiation exposure, and image quality. Additionally, there are AI-driven post-processing techniques, such as True Fidelity to further enhance image quality whilst lower radiation doses, and making diagnostic’s faster and more accurate. Through automating these tasks, medicals imagers have the potential to optimise critical aspects of patient care.
AI excels at automating repetitive tasks and time-consuming tasks, allowing professionals to focus on more complex tasks.
AI for Clinical Research
In clinical research, AI can play a crucial role in data transformation, allowing the user to streamline various processes – improving efficiency and accuracy. AI tools can automate data preprocessing tasks, such as data cleaning, normalising, and integrating large datasets from multiple images sources. This ensures consistency across diverse data formats, significantly reducing the manual effort involved, and maintaining high data quality. Additionally AI can accelerate data annotation by offering automated or semi-automated labelling techniques, which is beneficial for identifying and segmenting regions of interests in medical images. Another benefit is the enhancement in privacy protection that AI could offer, through automated de-identification methods, which ensure compliance with ethical regulators. Through the optimisation of these tasks, AI greatly supports clinical researchers in preparing reliable, highly quality datasets.
Trustworthiness of AI Systems
Another frequent concern in the adoption of AI is its trustworthiness. Trust is a critical factor for the successful integration of AI in medical imaging, as both patients and healthcare professionals need to be confident that they can rely on these systems for accurate and ethical results. To establish trust, AI systems must prioritise transparency, explainability, and reliability. Transparency requires clear documentation of how AI models process data, avoiding “black box” models and allowing radiologists and clinicians to audit the decision-making process.
Explainability ensures that the AI-generated insights are easily understood by clinicians and can be communicated to patients in a meaningful way. Lastly, reliability is key—AI systems must consistently perform well across different clinical settings and patient populations, reducing bias and ensuring reproducibility. Additionally, to uphold ethical standards, AI must be trained on diverse datasets to prevent the perpetuation of healthcare disparities, ensuring equitable treatment for all patients.
Final Thoughts
AI and Machine Learning have greatly improved automation and efficiency in medical imaging, allowing professionals to focus on more complex tasks. In research, AI has the potential to streamline data processing and maintain high data quality. However, trust in AI is essential for its widespread adoption, requiring transparency, explainability, and reliability. When used ethically and responsibly, AI can serve as a valuable tool to support, not replace, healthcare professionals, and ultimately improve patient outcomes.
Sources and helpful links
https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.16188
AI and Machine Learning in Medical Imaging: A 2023 Perspective
https://link.springer.com/content/pdf/10.1186/s12913-024-10928-x.pdf
https://www.sciencedirect.com/science/article/pii/S1939865423000796
https://www.gehealthcare.com/products/truefidelity
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-021-01630-7
https://about.cmrad.com/articles/the-ultimate-guide-to-preprocessing-medical-images-techniques-tools-and-best-practices-for-enhanced-diagnosis
https://link.springer.com/article/10.1007/s10278-019-00232-0
https://arxiv.org/pdf/2410.12402
https://ejrnm.springeropen.com/articles/10.1186/s43055-024-01356-2
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-01332-6
https://insightsimaging.springeropen.com/articles/10.1186/s13244-019-0785-8
