AI Applications: Can Radiology Take Inspiration from the Industrial Sector?

AI is transforming radiology, offering both interpretative and workflow-enhancing tools. Inspired by sectors like automotive, retail and finance, radiology can adopt AI-driven practices to improve efficiency, personalisation and cost control. From tailored reports to smart scheduling and inventory management, cross-industry innovations show great potential. Strategic adoption is vital as radiology navigates growing economic and competitive pressures.

Key Points

• AI in radiology includes diagnostic tools and systems to optimise workflow and resource use.
• Other industries use AI for personalisation, automation and operational efficiency.
• Radiology can adopt AI practices from sectors like retail, finance and manufacturing.
• Customised reports and smart scheduling can improve patient experience and reduce delays.
• Strategic integration of AI is essential for radiology to remain competitive and cost-effective.

Public Perception of AI

Nowadays, artificial intelligence (AI) is widely used and has become a trendy buzzword to highlight technological products. Beyond this strong marketing appeal, AI is routinely used in our everyday lives. It enables self-driving features in vehicles, voice and facial recognition on smartphones and computers, fraud detection systems, automated trading tools and entertainment algorithms such as those used by social media platforms (Rodrigues et al. 2023). In the healthcare sector, AI promises major advancements, especially in medical imaging. In this discipline, interpretative AI solutions are used in clinical practice to detect or quantify anomalies. Such solutions have been widely studied and are the subject of numerous publications. Non-interpretative solutions, which focus more on optimising workflows and resources, are also emerging on the market (Tadavarthi et al. 2020).

With such a wide range of tools available, one may ask: is it truly feasible to integrate all these solutions into the day-to-day operations of medical imaging departments? Can the radiology field draw inspiration from other industries to optimise its workflows? This article aims to explore some possible avenues for reflection by extrapolating practices already implemented in sectors other than radiology.

Issue of AI for Radiology Institutes

As with previous technological innovations in medical imaging, the emergence of artificial intelligence marks a decisive turning point for radiology. However, unlike other industrial sectors, the implementation of AI in radiology has not yet been fully democratised nor adopted across all institutions. Yet the economic issues are substantial, especially in an increasingly competitive environment and under growing pressure to reduce costs.

The emergence of AI is driving major transformations in radiology not only in the profession itself, but also across the entire radiology ecosystem, with significant implications for the management and strategic direction of healthcare institutions. This strategic shift must be approached with discernment to avoid the fate of certain emblematic companies that failed to anticipate technological disruption and lost their competitive edge in a rapidly evolving market (Figure 1).

The case of Kodak is a particularly striking example of a poorly executed strategy. Once a pioneer in photography, the company remained overly committed to analogue film and failed to adapt to the digital era—ultimately disappearing in favour of more agile and forward-looking competitors (Tellier 2023).

A similar parallel can be drawn with Volkswagen, currently facing serious challenges after missing its transition to electrification. This lack of responsiveness and strategic foresight cost the company its position as a global leader. Once highly prosperous, Volkswagen is now confronting substantial financial pressures (Kehkasha Arora et al. 2024).

The Use of AI in Various Areas of Industry

Beyond its technological dimension which has already revolutionised many aspects of our daily lives, AI is driving major economic disruption, with an estimated global impact of more than €14.1 trillion ($15.7 trillion) by 2030. Of this, €5.9 trillion ($6.6 trillion)is expected to result from productivity gains, and €8.2 trillion ($9.1 trillion) from increased consumer demand for AI-enabled products and services (Rao & Verweij 2017).

Contrary to common belief, AI is already deeply embedded in our everyday lives (Figure 2). In the automotive sector, technologies such as autopilot systems, adaptive cruise control, distance monitoring and parking assistance are now standard in newer vehicle models and are all based on AI. In addition, AI-powered software can predict mechanical failures and help reduce traffic congestion. In retail, AI solutions enable product personalisation based on customer preferences, targeted advertising through machine learning algorithms and improved inventory management, thereby saving space and reducing costs. In the financial sector, AI is already firmly established. It is used to deliver personalised financial planning, detect fraud, identify money laundering schemes and automate various back-office processes. In manufacturing, AI applications can correct production line errors, optimise supply chains and enable on-demand production. These solutions lead to higher product quality and shorter delivery times for customers. Finally, in marketing, communication and entertainment, AI enhances content archiving and recommendation systems, supports content creation tailored to consumer preferences and enables personalised advertising. As a result, customers benefit from more relevant content and save time when searching for products or information.

Application of AI in Medical Imaging

As discussed earlier, AI solutions applied to radiology (Figure 3) are primarily applied in diagnostic. Without being exhaustive, these include software for the detection of breast cancer, pulmonary nodules, fractures, cerebral haemorrhages and many others (Tadavarthi et al., 2020), while non-interpretative solutions focus on optimising workflow. Current software can adjust imaging protocols based on clinical demands (Brown & Marotta 2017), prioritise examinations according to urgency (Richardson 2021), display studies according to radiologists’ preferences, use voice recognition to streamline report dictation (Lakhani et al. 2018) and organise and structure radiology reports (Syed & Zoga 2018).

The Potential of AI in Radiology

By taking inspiration from other sectors, it is possible to envision new AI-driven solutions aimed at further optimising workflow and reducing waiting times for both patients and referring physicians (Figure 4). For example, depending on the referring physician, an AI system could adapt the vocabulary and terminology used in radiology reports to make them more accessible. A report intended for a general practitioner should not appear overly technical, while one addressed to a specialist should avoid being overly simplistic. This approach mirrors the personalisation algorithms used in the entertainment industry, which tailor content to individual user profiles.

Drawing from the manufacturing sector, AI applications able of identifying delays in report validation and transcription could help to prevent disruptions in patient care. Similarly, inventory management solutions for contrast agents and consumables would help avoid overstocking costly items of interventional radiology such as biopsy needles, catheters, and trocars. Just as in the financial or retail sectors, AI could also be used to propose personalised appointment schedules based on a patient’s availability and existing medical bookings within a clinic or hospital. Such systems would offer significant time savings for patients by minimising long wait times between consultations.

Conclusion

The field of radiology is undergoing profound transformation, particularly with the advent of AI, which is redefining both clinical practice and strategic management within imaging centres. This disruptive technology is already integrated into our daily lives with significant impacts on how we consume products and services. As in other industries, stakeholders and managers in radiology must not miss this turning point. They need to approach the transition to AI strategically and fully integrate it into both their clinical operations and organisational governance. In an increasingly competitive environment marked by economic pressures, the adoption of AI represents a critical challenge and a major opportunity for the future of radiology. To evolve, the medical imaging sector must also draw inspiration from AI-driven innovations successfully implemented in other industries.

Conflict of Interest

None