AI and Radiology: A Deep Dive into OpenAI's GPT-4 Advancements

 Open AI has been rocking the world since 2022, and with the looks of it, there is no stopping. Don't be surprised that professionals have been praising the model for its intuitive capabilities, such as generating articles and codes and even helping in areas like healthcare, such as diagnostics, treatment planning, and patient engagement. 

While AI already contributes to many other fields, such as image analysis and drug interaction, it has been widely recognized for its potential in NLP tasks. 

In today's blog, we'll talk about the different aspects of GPT-4 in terms of radiology and how it changes its landscape.

Let's start with What is GPT- 4?

Suppose you're not familiar with GPT. GPT is a Generative Pre-Trained Transformer 4 language model that is built with deep learning technology in order to generate human-like output. You'll be surprised to know that it doesn't only produce text but can do many other things, like create codes, poetry, technical documents, stories, essays, etc. And this is how it stole the spotlight in natural language processing. 

The first GPT model was introduced in 2018 by OpenAI, followed by GPT-2 in 2019, GPT 3 in 2020, GPT 3.5 in 2022, and the latest GPT 4 in 2023. 

Coming back to the medical field. One of the best technological advancements involves GPT-4's impressive performance on the medical competency exams and benchmark datasets. Along with this, GPT-4 has also shown some potential to enhance medical consultations, which portrays a promising perspective for healthcare innovation.

Advancing radiology AI for real troubles

In a recent research paper published by Microsoft named

'Exploring the Boundaries of GPT-4 in Radiology' deeply explores the subject of GPT-4's potential in healthcare and radiology's abilities and limitations. 

A little about radiology

It's a field that helps diagnose disease stages with treatment through imaging technologies such as computed tomography, x-ray and MRI. 

Mircosoft's research included a thorough examination and fallacy analysis framework to evaluate GPT-4's capabilities to examine and analyze radiology reports. This examination combined the following conditions: common language understanding and generation tasks in radiology, such as disease categorization and findings summarization. 

This framework was conceptualized, developed and executed with the help of board-certified radiologists to tackle more complex and problematic real-world situations in radiology, which moved beyond mere metric scores. 

They were also able to investigate a few areas like few-shot, chain-of-thought, and effective zero-, thus prompting GPT-4 throughout multiple radiology tasks in order to build more reliability on GPT-4 outputs. 

Every task that was assigned to GPT-4 was performed with an over-expected benchmark against prior GPT 3.5 models and state-of-the-art radiology models. 

The results were mindblowing as new state-of-the-art performance in some tasks achieved 10% enhancement with existing models. It was also able to summarize reports; in some cases, it was even preferred over those written by professional radiologists. 

Apart from all these favourable achievements, GPT-4 also has the potential to structure radiology reports automatically. These reports are hard to interpret because they're complex and unstructured, including the radiologist's interpretation of medical images like X-rays, patients' history, etc. Microsoft's research states that putting these reports can enhance standardization and consistency in disease characterizations, making it simpler to diagnose by different healthcare providers and making things more accessible for research purposes and other quality enhancement endeavours. It also has the potential to sustain further efforts to augment real-world data (RWD) and its benefits for real-world evidence. This, in turn, will complement comprehensive clinical trials and also advance the application of research findings into clinical practice.

Translating Medical Report

GPT-4  also demonstrates opportunities to translate medical reports into more understandable formats for patients and health professionals. This could help with patient engagement and education, making it simpler for their carers to participate actively in their healthcare journey. 

In conclusion, GPT-4 has the potential to transform radiology by assisting healthcare experts in their daily tasks. 

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