Plain film (X-ray) reporting and AI at RSNA 2023

Plain film (X-ray) reporting and AI (Brief Primer)

The evolution of artificial intelligence (AI) in plain film (X-ray) reporting marks a significant step in radiology's digital transformation.

This change is especially relevant given the prevalence of radiographs as radiology departments' most common imaging examinations. Their high volume makes them an ideal target for AI implementation, with potential benefits in efficiency and quality.

Radiology's digital journey, starting in the 1970s, transitioned from film-based to digital radiography, managed by Picture Archiving and Communication Systems (PACS) --this digital shift laid the foundation for AI integration in radiology, aiding in collecting and interpreting a vast volume of digital images.

Despite the promise of AI, mainly through deep learning and convolutional neural networks (CNNs), its clinical adoption has been gradual. Challenges include:

Integrating AI into existing workflows.
Establishing explicit business models.
Demonstrating substantial clinical benefits over traditional methods.

Radiologists' work, encompassing image analysis, report generation, and consultations, often requires manual comparison of current and previous studies, an aspect only partially addressed by AI.

AI tools in radiology, such as computer-aided detection (CADe) and computer-aided diagnosis (CADx), have diverse applications. However, their effectiveness in improving diagnostic accuracy and clinical outcomes, particularly in screenings like breast cancer and diabetic retinopathy, is an area of ongoing research.

The full potential of AI in radiology is yet to be realized, hindered by challenges like data quality and diversity, extensive image labeling requirements, and image quality variability.

Ethical, transparency, and explainability issues in AI healthcare applications also pose significant concerns.

As the field continues to evolve, integrating data from radiology, pathology, and genomics hints at the emergence of integrated diagnostic services. This evolution will likely redefine the radiologist's role in patient care and medical decision-making, heralding a new era in healthcare diagnostics.

Attend Booth Session 5 at RSNA 2023 to explore AI in plain film reporting and its future in radiology. Join Nina Kottler, Associate Chief Medical Officer of Clinical AI and VP of Clinical Operations at Radiology Partners, on November 29, 2023, at 1:30 pm CST. Learn about AI's impact on radiology essentials and its role in enhancing the precision and efficiency of X-ray interpretation. Engage in this transformative discussion on the future of radiological practice.