Blog Economie Numérique - AI Deployment in Medicine – A Business Opportunity in Radiology?

The exponential proliferation and dynamic development of numerous new AI models in the current market (as of 2025) has led to widespread applications across diverse segments and industries. Developers and users of AI technologies increasingly recognize the advantages of AI in reducing opportunity costs of time and energy, while simultaneously increasing overall utility. One particularly relevant segment is the application of AI in the medical domain.

According to Guo et al. (2024), AI—particularly through deep learning mechanisms—is already being deployed in radiology for the diagnosis of X-ray images. Chest radiography and related imaging modalities are among the most frequently used image-guided diagnostic tools worldwide, accounting for approximately two billion imaging procedures annually.

AI companies seeking to expand beyond mass-market applications such as AI chatbots, generative AI, and other language or creative AI models are increasingly exploring the development of novel deep learning architectures tailored to the medical sector. Radiology, in particular, represents a promising field for such expansion. The underlying concept involves the AI-assisted evaluation of digital X-ray records within clinical radiology. Physicians could thereby be supported by an “AI companion” performing an assistive function in the interpretation of radiographic images, including bone fractures, musculoskeletal injuries (muscles and tendons), and other radiological indications.

But why?

Guo et al. (2024) report that especially junior radiologists and early-career physicians experience substantial daily workloads and elevated stress levels. They are required to produce numerous radiological diagnoses within short time frames. Consequently, the cognitive energy expenditure necessary to maintain sustained 100% attentional focus for accurate interpretation of radiographic images is considerable. A critical prerequisite for reliable diagnostic performance is extensive clinical experience—something that younger practitioners are still in the process of acquiring.

The deployment of AI could therefore reduce subjective human error and improve efficiency within radiological practice. A particularly relevant application domain concerns highly prevalent and potentially fatal diseases, such as thoracic pathologies (Guo et al., 2024). As stated by Guo et al. (2024):

“A computer system to interpret chest radiographs as effectively as practising radiologists could thus provide substantial benefit in many clinical settings, from improved workflow prioritization and clinical decision-making support to large-scale screening and global population health initiatives.”

Artificial intelligence, especially deep learning approaches, is already being utilized in the field of assisted radiology. Initial implementations in lower-tier market segments have demonstrated the use of automated diagnostic algorithms for the classification of pulmonary tuberculosis. However, comparable algorithmic maturity has not yet been achieved across a broader spectrum of differentiated disease entities. Guo et al. (2024) argue that AI-based assistants (deep learning models) remain significantly underdeveloped in areas such as pneumonia and pneumothorax detection. These domains may therefore represent strategically attractive opportunities for AI investors seeking to develop advanced deep learning architectures and complementary diagnostic algorithms to address existing market gaps in an efficient and clinically meaningful manner.

A noteworthy example is the statistical analysis conducted in the study by Guo et al. (2024). Three cohorts were selected: Group 1 (29 senior physicians), Group 2 (32 intermediate-level physicians), and Group 3 (50 junior physicians), resulting in a total sample size of N = 111. The deep learning model employed was the AI model “Fast R-CNN.”

The study’s findings indicate that radiologists across all experience levels demonstrated improved diagnostic performance when supported by AI. While performance gains were observed in junior, intermediate, and senior cohorts alike, the study does not provide conclusive evidence that one experience group benefited disproportionately more than the others.

The study reports a statistically significant improvement in overall diagnostic performance when AI assistance was provided. The mean diagnostic score increased from 597 (without AI) to 619 (with AI), with p < 0.001, indicating statistical significance (Guo et al., 2024). In addition, a substantial reduction in interpretation time was observed, decreasing from 3279 seconds to 1926 seconds (Guo et al., 2024). References to both improved efficiency and statistically significant performance enhancement are therefore empirically justified.

Importantly, AI assistance did not uniformly outperform unaided radiologists across all diagnostic categories. Radiologists alone demonstrated superior performance in specific findings, including aortic calcification, calcification, cavitation, nodules, pleural thickening, and rib fractures (Guo et al., 2024). Any generalized claim suggesting that AI universally improves all diagnostic categories would therefore constitute an overstatement of the study’s findings.

The authors explicitly acknowledge several methodological limitations. First, only frontal chest radiographs were evaluated; up to 15% of thoracic diagnoses may require lateral views for accurate assessment (Guo et al., 2024). Second, the dataset originated from a single institution, limiting the external validity and generalizability of the results (Guo et al., 2024). Third, the experimental design does not fully replicate real-world clinical workflow conditions, which may influence practical applicability (Guo et al., 2024).

The study by Guo et al. (2024) provides empirical evidence that AI-assisted radiological assessment can significantly improve diagnostic performance and substantially reduce interpretation time in chest radiography. The results suggest that AI functions most effectively as a supportive clinical tool rather than a replacement for physician expertise.

However, the findings must be interpreted within the methodological constraints of the study. The use of a single-institution dataset, restriction to frontal chest radiographs, and an experimental design that does not fully reflect routine clinical environments limit the immediate generalizability of the results. AI deployment in radiology appears to offer measurable efficiency gains while maintaining diagnostic standards, thereby representing a plausible business opportunity, provided that future systems address current limitations and undergo rigorous clinical validation.

Guo, L., Zhou, C., Xu, J. et al. Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance. J Digit Imaging. Inform. med. 37, 922–934 (2024)