Beyond Image Interpretation: Impact of Natural Language Processing in Radiology

Beyond Image Interpretation: Impact of Natural Language Processing in Radiology

Natural language processing (NLP) is a field of computer science that deals with the interaction between computers and human (natural) languages. The field has gained significant momentum recently due to the widespread adoption of language models in many sectors. Leon Bergen, Assistant Professor of Linguistics at UC San Diego and Ferrum Health advisor, states: “In radiology, the potential of large language models like chatGPT and Google Bard is clear. While full integration is on the horizon, these models promise to address report inconsistencies, streamline workflows, and enhance patient communication.”

Streamlining study protocolling

Protocoling refers to the process of determining the most appropriate imaging study for a patient based on their clinical symptoms, history, and other factors. Currently, radiologists manually review and protocol every imaging study that is ordered, which is time-consuming among their many other responsibilities. Advancements in NLP can streamline and automate the protocoling process using clinical information from the medical record and the history provided by clinicians. It may also serve as an initial feedback mechanism for clinicians when they order an imaging study, helping them make more informed decisions about what study to order based on their clinical inquiry. While radiologists should still be involved in the protocoling process, NLP can significantly reduce the time investment required, enabling them to operate more efficiently.

Summarizing patient clinical course 

Though clinicians are responsible for entering a “reason for the exam” for every radiology study, they order, important clinical details are often omitted. Consequently, radiologists frequently find themselves consulting the electronic medical record (EMR) to obtain an up-to-date and clinical history, a process that can be time-consuming. NLP can be used to summarize the patient’s relevant clinical course from the EMR, empowering radiologists to better understand the patient’s relevant clinical course. By streamlining this workflow, NLP not only enhances the efficiency of radiologists but also equips them to deliver more clinically relevant interpretations.

Compiling key findings from prior imaging

It is crucial that all key findings from prior imaging and radiology reports are appropriately followed up on. This task can often prove to be time-intensive, particularly when dealing with patients who have an extensive imaging history. NLP can be used to summarize and highlight the key findings of prior imaging, which can help radiologists readily identify any findings that need to be addressed. This streamlined approach not only improves the radiologist’s efficiency but serves as a safeguard against missed diagnoses.

Standardizing the language of radiology reports

Radiology reports are often unstructured and can be burdensome to interpret by clinicians. NLP can be used to improve the interpretability of radiology reports by converting unstructured reports dictated by different radiologists to structured reports that are organized in the same consistent manner. Structured reports can be more easily searched and analyzed, which can help clinicians and other radiologists find the information they need more quickly and easily. Standardizing reporting also facilitates data mining, which will be key in the future of radiology AI. 

Creation of patient-centered radiology reports

The 21st Century Cures Act provides patients with near-immediate access to their radiology reports, oftentimes even before their referring clinicians can interpret and discuss the results with them. This can lead to misunderstandings and undue anxiety over benign findings discussed in the report. NLP may offer a solution by translating clinical radiology reports into patient-centered reports written in straightforward, plain language that patients can easily understand. These reports can provide essential context for findings such as renal cysts and lung nodules, offering reassurance about their benign nature. This approach not only promotes patient empowerment and autonomy but also facilitates their comprehension of crucial medical information, enabling them to make more informed decisions about their health and treatment options. 

Streamlining Billing and Coding

In addition to its role in improving clinical workflows, NLP holds great promise in the realm of billing and coding within radiology practices. The billing and coding process is intricate and requires significant resources to ensure accurate reimbursement. NLP algorithms can parse through radiology reports, extract crucial billing and coding information, and then cross-reference the extracted data with insurance policies to ensure that billing is in compliance and will be approved. Automation of billing and coding tasks not only accelerates the revenue cycle but also minimizes the administrative burden on radiologists, billing staff, and the healthcare system. By streamlining this process, NLP can improve overall operational efficiency and financial sustainability, allowing healthcare providers to allocate more resources to patient care.

There are a number of challenges associated with using NLP in radiology, including:

  • The complexity of radiology reports.
  • The variability in the way that different radiologists write reports.
  • The need to ensure that the results of NLP algorithms are accurate and reliable.

Despite these challenges, NLP is a promising technology that has the potential to revolutionize the field of radiology and streamline workflow. As NLP technology continues to develop, we can expect to see even more innovative applications of NLP in radiology in the years to come.

Picture of Varun Danda

Varun Danda

Varun Danda, MD, is an integrated interventional radiology resident at the Icahn School of Medicine at Mount Sinai, NY. He has a passion for medical technology, focusing on artificial intelligence, augmented reality, and medical devices in radiology.

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