Is the future of radiology about AI, or about connectivity?

Artificial intelligence has become one of the biggest conversations in healthcare technology. Wherever you look, AI is being presented as the next major step forward for radiology, from clinical decision support and image analysis to workflow optimisation and reporting. There is no doubt that AI has enormous potential. But perhaps we are asking the wrong question?

Rather than asking whether AI is the future of radiology, perhaps we should be asking whether connectivity is what will allow that future to work. Radiology has never been short of innovation. Imaging technology has advanced dramatically, reporting has become increasingly digital, voice recognition has transformed the way clinicians work, and cloud technology is changing how information and applications can be accessed. Yet, despite all of this progress, many of the pressures facing radiology departments remain familiar. Imaging volumes continue to increase, reporting capacity is under pressure, specialist resources are limited and healthcare organisations are increasingly expected to work across organisational and geographical boundaries.

The challenge is therefore not necessarily a lack of technology. It is how effectively that technology works together. Most radiology environments are not built around a single system from a single supplier. Organisations often operate a combination of RIS, PACS, EPR, VNA and other clinical applications, with newer technologies sitting alongside systems that may have been in place for many years. Replacing everything is neither realistic nor necessarily desirable. The more important question is how those systems can communicate and support a common workflow.

This is where connectivity and interoperability become increasingly important. At Soliton IT, our approach has always been centred around integration and flexibility, allowing organisations to enhance their existing radiology environments rather than necessarily having to start again. Our solutions are designed to work alongside existing systems and support the way radiology departments actually operate.

This becomes particularly important as regional and multi-site imaging networks continue to develop. The ability to share work, expertise and information across organisational boundaries can help make better use of the resources already available. A radiologist should not necessarily need to know where a piece of work originated, which system holds it or which organisation owns the resource being used to report it. The technology should make that process feel as seamless as possible.

That is the real value of interoperability: making complexity happen behind the scenes, rather than putting it in front of the user. This does not mean that AI is unimportant. Quite the opposite. AI has the potential to become an extremely valuable part of the radiology workflow, supporting prioritisation, reporting, decision-making and the management of increasing workloads. But AI does not operate in isolation. For an AI application to deliver meaningful value, it needs access to the right information, at the right point in the workflow, and its output needs to reach the right person or system. If data is fragmented or workflows are disconnected, the benefits of even the most sophisticated technology can be restricted.

The same principle applies to voice recognition. Modern voice recognition can significantly reduce manual transcription and make reporting more efficient. AXREM’s recent position on cloud-based medical voice recognition highlights the potential for technology to become more accessible, integrated and adaptable, while also emphasising the importance of interoperability, security and measurable improvements to workflow. The technology itself is only part of the solution. How it fits into the clinical workflow is just as important.

One of the most interesting changes in healthcare technology is the shift from asking clinicians to adapt to software, towards designing software that adapts to the way clinicians work. Radiology departments are not all the same. Workflows differ between organisations, sites and even individual users. Technology therefore needs to be flexible enough to support those differences rather than forcing departments into a single way of working.

This is particularly important when introducing new technology alongside existing infrastructure. Digital transformation should not automatically mean starting again from scratch. Sometimes the most effective solution is to enhance what is already there, making existing investments work harder while creating a path towards future development.

Perhaps the biggest change in thinking we need to make is how we measure digital transformation. Success should not be measured by how many new technologies an organisation has purchased. It should be measured by what those technologies enable. Can a radiologist report more efficiently? Can workloads be shared more effectively? Can specialist expertise be accessed across organisational boundaries? Can existing systems continue to deliver value? Can patients receive their results sooner? These are the outcomes that matter.

The future of radiology will certainly involve AI. It will involve cloud technologies, advanced analytics, voice recognition and increasingly sophisticated digital platforms. But none of these technologies exists in isolation. The real opportunity is to create a connected environment in which they can work together securely, efficiently and around the needs of the people using them.

Radiology does not need technology for technology’s sake. It needs technology that removes friction, supports collaboration and gives clinicians more time to focus on what matters: delivering the best possible care for patients.

For years, the industry has asked what the next technology can do for radiology. Perhaps the more important question now is:  how can we make all of our technology work better together?

That may be where the real revolution in radiology begins.

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