AI-powered CAD: Integrating intelligent analysis into routine healthcare

Computer-Aided Detection (CADe) and Diagnosis (CADx) have progressed from rule-based, traditional image processing to sophisticated computational tools that enhance diagnostic precision and clinical decision-making. These approaches have been developed to assist the interpretation of medical images (including CT, mammography, MRI, and PET), helping radiologists identify and characterize abnormalities with greater accuracy. These innovations are leading to more efficient and improved patient care. A prime example is early detection, which remains the most effective strategy for improving patient survival rates. For instance, prostate cancer has a 5-year survival rate of approximately 98% when diagnosed at an early stage. However, this rate decreases to 30% for patients with distant stages of the disease¹. 

The continuous advancement of CAD technologies, including Computer-Aided Detection, Diagnosis, and Triage, has transformed medical imaging by improving lesion identification, classification, and workflow optimization. Nevertheless, there are important differences from the regulatory standpoint when approaching the different clearance strategies, that directly impact the investment needed for commercialization, as well as the level of clinical validation and claims that can be used for marketing activities. Our objective with this publication is to summarize the main differences. 

Computer-Aided Detection (CADe) 

CADe functions as a “second reader,” detecting and highlighting suspicious regions in medical images, such as subtle microcalcifications on mammograms. These systems overlay markings, such as bounding boxes, contours, or heatmaps, to assist radiologists in identifying abnormalities. As an adjunctive tool, CADe software enhances diagnostic precision by alerting radiologists to regions of interest (ROIs) that might be overlooked, improving accuracy and reducing variability. 

CADe devices integrate pattern recognition and data analysis, extracting features from radiological data to direct attention to potential abnormalities during image interpretation. 

All CADe devices require standalone assessment, including case- and lesion-level metrics. Performance is typically evaluated using the Dice similarity coefficient, Intersection over Union (IoU), and mean distance. Some systems must also demonstrate clinical improvement through multi-reader, multi-case studies (e.g., Siemens’ Syngo CT lung CAD; Medtronic’s GI Genius™ intelligent endoscopy module; ImageChecker by Hologic).   

Computer-Aided Diagnosis (CADx) 

CADx is designed to analyze and characterize lesions on radiological images that are suspicious of cancer or other pathological conditions. Unlike CADe, CADx does not automatically detect lesions but instead processes user-identified regions of interest, extracting relevant data such as size, density, and morphology to assist in diagnosis. A key distinction is that a CADx-only device does not autonomously identify lesions; these must be manually selected by the user. 

CADx systems process and present this extracted information to clinical users to aid diagnosis. To ensure efficacy, these devices must demonstrate clinical improvement through a multi-reader, multi-case study (e.g., Optellum’s lung nodule malignancy score; iCAD’s ProFound AI by iCAD Inc; Optellum by Optellum Inc.).  

CADe/x Devices 

CADe/x systems combine automated lesion detection and characterization, necessitating standalone assessment with lesion-level metrics and multi-reader, multi-case studies to confirm clinical significance (e.g., QP-Prostate^® from Quibim). These systems serve as concurrent reading aids, assisting radiologists in identifying and characterizing abnormalities while improving workflow efficiency. 

AI-based CADe/x solutions address key imaging challenges such as missed cancers during screening, excessive recalls, low sensitivity, and inter-reader variability. By enhancing detection accuracy and consistency, these systems contribute to early diagnosis and more reliable clinical decision-making. 

Computer-Aided Triage (CADt) 

CADt is an AI-based prioritization software that accelerates clinical response by flagging critical cases (e.g., suspected pulmonary embolism). However, its primary function is triage, ensuring that urgent cases receive immediate attention.  

While CADt improves workflow efficiency, it is a complementary tool to CADe and CADx rather than directly influencing diagnostic accuracy. CADt systems prioritize suspicious cases for review but do not annotate images or pinpoint findings. Their primary AI function is a binary flagged/unflagged indicator, enabling clinicians to streamline case prioritization while maintaining complete interpretative control (e.g., AnnaliseTriage; Briefcase Triage by Aidoc). 

Understanding CADe, CADx, and CADt 

Why CADe/CADx Offers Greater Clinical Value 

CADe/CADx systems enhance diagnostic precision by reducing interpretation errors and improving lesion detection and characterization. These AI-driven solutions refine clinical decision-making, minimizing unnecessary biopsies, lowering patient risk, and reducing healthcare costs. While CADt leads in regulatory approvals, CADe/CADx holds greater clinical value by enhancing diagnostic accuracy and minimizing unnecessary interventions. Compared to CADt, which primarily accelerates triage, CADe/CADx offers a comprehensive analytical approach that directly contributes to improved patient outcomes, reinforcing their higher clinical and economic value despite a longer regulatory process. 

QP Prostate^® embodies this synergy by integrating detection and characterization within a single workflow. The software segments the prostate gland and extracts key morphological and functional parameters, streamlining prostate cancer assessment from early detection to final grading. 

Despite the increasing adoption of CADt due to its expedited regulatory pathway, CADe/CADx remains the cornerstone of AI-powered medical imaging, offering robust diagnostic support, while CADt provides critical advantages in emergency settings. The integration of detection and diagnosis within a single platform, as seen in QP Prostate^®, delivers a comprehensive evaluation, leading to higher diagnostic accuracy, fewer unnecessary interventions, and superior patient care.  

Regulatory trends indicate a steady rise in CAD approvals since 2016, with significant growth in recent years, highlighting the increasing availability of AI-driven imaging solutions³. However, regulatory clearance does not necessarily equate to widespread clinical adoption or commercial success, reinforcing the need for rigorous validation to demonstrate clinical efficacy and real-world impact. 

From a strategic standpoint, regulatory frameworks prioritize clinical impact and risk stratification, allowing for expedited approvals of triage tools while requiring more rigorous validation for diagnostic solutions. Post-market oversight then focuses on ensuring continued safety, effectiveness, and real-world clinical utility. The regulatory framework differentiates between minor software updates—validated internally—and significant algorithmic enhancements, which require full regulatory review to ensure safety and efficacy. Additionally, CADt solutions often rely on retrospective validation, whereas CADe/CADx solutions typically undergo rigorous multi-reader studies, reflecting their distinct regulatory pathways and clinical validation requirements. 

If data quality, validation methodologies, and regulatory compliance are addressed, the seamless integration of CADe, CADx, and CADt into clinical workflows will advance precision medicine. These AI-driven technologies are now embedded within PACS systems, enabling automated quality control aligned with standardized imaging guidelines and improving diagnostic reproducibility. 

QP Prostate^® exemplifies this advancement. It utilizes pathology-trained AI algorithms to detect clinically significant prostate cancer lesions using biparametric MRI (T2W and DWI). These innovations are redefining the standard of care by enhancing diagnostic precision and improving patient management in oncology and beyond. 

As CAD systems evolve, ensuring diverse, high-quality datasets, seamless integration into hospital IT systems, and continuous real-world validation will be crucial for maintaining performance reliability and widespread adoption. 

References 

1. Lin J, Nousome D, Jiang J, Chesnut GT, Shriver CD, Zhu K. Five-year survival of patients with late-stage prostate cancer: comparison of the Military Health System and the U.S. general population. Br J Cancer. 2023 Apr;128(6):1070-1076. doi: 10.1038/s41416-022-02136-3. Epub 2023 Jan 6. PMID: 36609596
2. Yuba M, Iwasaki K. Performance evaluation methods for improvements at post-market of artificial intelligence/machine learning-based computer-aided detection/diagnosis/triage in the United States. PLOS Digit Health. 2023 Mar 8;2(3):e0000209. doi: 10.1371/journal.pdig.0000209. PMID: 36888573; PMCID: PMC9994700. 
3. McNamara, S.L., Yi, P.H. & Lotter, W. The clinician-AI interface: intended use and explainability in FDA-cleared AI devices for medical image interpretation. npj Digit. Med. 7, 80 (2024). https://doi.org/10.1038/s41746-024-01080-1