Article Information

Sadhana Kalidindi^1*,RVPrasanna Vadana²

¹Research Fellow, ARI Academy, Hyderabad, India

²Global Fellow, Department of Radiology, University Hospital, Sussex, UK

^*Corresponding Author: Sadhana Kalidindi, Research Fellow, ARI Academy, Apollo Health Street, Cancer Block, PET CT Center City, Jubilee Hills, Hyderabad, India.

Received: 14 September 2024; Accepted: 25 September 2024; Published: 24 October 2024

Citation: Sadhana Kalidindi, RV Prasanna Vadana. Envisioning the Future: The Role of Large Language Models in Radiology Education. Journal of Radiology and Clinical Imaging. 7 (2024): 74-77.

Abstract

Keywords

Large Language Models (LLMs); Radiology Education; Artificial Intelligence in Medicine; Interactive Learning Modules; Personalized Education; Natural Language Processing (NLP).

Article Details

1. Introduction

Radiology as a specialty has seen an exponential growth in the last few decades. This is due to multitude of factors including a steep rise in the volumes of cases, a much wider spectrum of imaging, and increasing complexity. This has resulted in overburdened radiology departments and radiologists globally. The situation is further compounded by an increase in non-interpretative work undertaken by radiologists [1].

These trends in workload patterns that led to increasing levels of burnout among radiologists and radiology educators have had an adverse impact on radiology education [2]. Radiology being a specialty that has been at the forefront of innovation, several novel methods have been tried to improve the shortcomings in radiology education.

There has been a rapid shift towards online education over the last few years, mainly fuelled by the COVID-19 pandemic. Online lectures, webinars and video-conferencing platforms not only supplemented but in many cases replaced conventional teaching [3].

Radiology Education augmented by AI has been envisioned to provide a more precise and personalised education to trainees based on their interests, strengths and weaknesses. Both NLP tools and computer vision algorithms are considered to be useful in this context [4].

LLMs like GPT-4 and GPT-4o, with their advanced natural language processing and machine learning capabilities, present an opportunity to revolutionize radiology education, making it more accessible, interactive, and personalized.

2. The Current Landscape of Radiology Education

Radiology education relies heavily on the acquisition of theoretical knowledge, practical skills, and the ability to apply this knowledge in clinical settings. However, the exponential growth in medical knowledge, coupled with advancements in imaging technology, places a significant learning burden on students and professionals. Additionally, the heterogeneity in learner pace and style necessitates a more customized approach to education.

The conventional methods of teaching were hampered substantially by limitations like availability of case materials and experienced educators. This is particularly true in developing countries with heterogenous training curricula and scarce resources. The COVID-19 pandemic has revolutionised radiology education by catalysing rapid progress in various forms of online education methodologies [5].

Artificial Intelligence (AI) and Machine Learning technology heralded a new chapter in the evolution of radiology. AI products that have been approved by the FDA include applications throughout radiology workflow [6]. The advent of LLMs has further transformed this space with a large range of additional capabilities, including enhancement and personalisation of radiology education.

3. Potential to transform radiology Education with LLMs

3.1 Interactive Learning Modules

LLMs can generate dynamic, case-based learning scenarios that mimic real-life radiological cases, offering students a hands-on learning experience. These modules can adapt to the learner's responses, providing instant feedback and tailored learning paths.

Figure 1 below is an example of an instant interactive learning module generated by GPT-4 for a 3rd year radiology resident on Acute Cholecystitis

Example 1: 

Prompt: I am a 3rd year Radiology resident. Generate an interactive learning module for me on Acute Cholecystitis

GPT-4 Responses: Below are some screenshots from a comprehensive module immediately generated by GPT-4

Figure 1A: an example of an instant interactive learning module generated by GPT-4 for a 3rd year radiology resident on Acute Cholecystitis

Figure 1B: an example of an instant interactive learning module generated by GPT-4 for a 3rd year radiology resident on Acute Cholecystitis

3.2 Personalized Education

By analysing individual learning patterns and progress, LLMs can customize educational content, focusing on areas that require improvement. This personalized approach ensures that learners at different levels receive the support they need to advance their skills.

Figure 2 is an example screenshot of further interaction with GPT-4 in relation to the conversation shown in Figure 1

Figure 2: An example screenshot of further interaction with GPT-4 in relation to the conversation shown in Figure 1

3.3 Enhancing Research and Critical Thinking

LLMs can assist students and professionals in exploring the latest research findings, understanding complex methodologies, and developing critical thinking skills. It can summarize research articles, generate hypotheses, and even propose experimental designs, serving as an invaluable tool for both learning and innovation.

3.4 Simulation and Virtual Mentorship

Through realistic simulations, LLMs provide a safe environment for learners to practice interpretative skills without the risk of patient harm. Furthermore, its ability to provide mentor-like guidance and answer questions 24/7 ensures continuous learning opportunities outside traditional settings.

3.5 Virtual Radiology Assistants

LLMs can power virtual assistants that can guide students through complex radiological images, offering explanations and answering questions in real-time. This could significantly enhance understanding by allowing students to interact with the assistant to clarify concepts, explore different diagnoses, and understand the rationale behind certain imaging techniques

Figure 3 below is an example of GPT-4 acting as a virtual assistant to a radiology resident while they’re reviewing a complex CT scan

Prompt 1: I am looking at a CT scan which is showing small bowel obstruction and gall stones. Please prompt me on the various things I need to be looking for

GPT 4 Responses: Below are some screenshots of the responses from GPT-4

Figure 3: An example of GPT-4 acting as a virtual assistant to a radiology resident while they’re reviewing a complex CT scan

Prompt 2:  There seems to be air in the biliary tract along with small bowel obstruction and gall stones. What is the most likely diagnosis

GPT-4 Responses

3.6 Simulation of Patient-Radiologist Interactions

 LLMs can be employed to simulate conversations between radiologists and patients or other healthcare providers, improving communication skills critical for conveying diagnosis, discussing treatment options, and managing patient expectations.

3.7 Language Translation and Global Education

LLMs language capabilities can be leveraged to translate complex radiological content and educational materials into multiple languages. This democratizes radiology education, making high-quality resources accessible to non-English speaking students around the world, thus fostering a more inclusive global radiology community.

Figure 4 below is a screenshot of the same module demonstrated in earlier examples, generated in German language by GPT-4o

3.8 Augmented Reality (AR) Integration

Combining LLMs with AR technologies can lead to development of immersive learning experiences where students can interact with 3D models of anatomy and pathology. LLMs can provide real-time, context-aware information and guidance as students navigate through different layers of anatomy or pathological findings in an AR environment

Challenges and Considerations

While LLMs offer numerous advantages, their integration into radiology education requires careful consideration of several factors, including ensuring accuracy of medical content, addressing ethical concerns related to data privacy, and the need for human oversight to contextualize AI-generated content.

The potential for LLMs to produce fake and potentially harmful information based on their logic (i.e., hallucinations) is a significant drawback. Patients should be made aware of this when they receive such information [7].

Conclusion

The integration of LLMs like GPT-4 into radiology education has the potential to significantly enhance the learning experience, offering personalized, interactive, and comprehensive educational tools. By overcoming current educational challenges and leveraging the capabilities of LLMs, the future of radiology education can be reshaped to produce more skilled, adaptable, and proficient radiologists. As we explore the potential of this technological revolution in radiology education, it is imperative to navigate the integration of LLMs with consideration for its limitations and ethical implications, ensuring that the technology serves to augment rather than replace the invaluable human elements of education and patient care.

During the preparation of this work the authors used Chat GPT-4 and GPT-4o in order to improve readability of certain portions of this article. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

References

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