What’s Next for AI in Radiology?: Facilitating Data Orchestration Over Fragmentation

Artificial intelligence has become one of the most discussed topics in health care. Conversations often focus on the latest model, application or breakthrough use case. However, for many health-care leaders, the challenge is no longer determining whether AI has potential. The challenge is determining how to translate that potential into meaningful operational and clinical impact.

Most health systems have already moved beyond experimentation. They have piloted AI tools, implemented digital platforms and invested heavily in data infrastructure. Yet many of health care's most pressing challenges remain.

Clinician shortages continue to strain health systems. Patient demand continues to rise. Care delivery remains fragmented. Operational complexity often makes it difficult for teams to access the right information at the right time.

Can AI Improve Access to the Right Data at the Right Time for Radiologists?

In many ways, health care's challenge is no longer generating data. It is connecting and operationalizing it. This is where the next phase of health-care AI begins.

Health-care organizations are increasingly moving beyond questions of whether AI works and toward questions of how it can be deployed consistently, responsibly and at scale. Success will depend less on the sophistication of individual algorithms and more on an organization’s ability to embed AI into the day-to-day realities of care delivery.

For much of AI's early adoption, the focus was on point solutions. AI was applied to individual tasks such as analyzing an image, identifying patterns in data, prioritizing a worklist or supporting a specific clinical decision.

These applications remain important and continue to create value, but increasingly, health-care leaders are asking a different question:

How can AI help improve the performance of the health-care system itself?

The answer lies in moving beyond isolated applications toward connected, enterprise-wide workflows.

Can AI Help Overcome Hurdles with Fragmented Data?

Health care today remains highly fragmented. Information is often spread across departments, technologies and care settings. Clinicians frequently navigate multiple systems to access patient information, coordinate care and make decisions. At the same time, health systems face growing pressure to improve access, increase productivity and do more with limited resources.

The greatest opportunity for AI may not be creating another stand-alone application. It may be helping health-care organizations connect data, workflows and people more effectively.

When combined with interoperable systems, connected platforms and thoughtfully designed workflows, AI can help surface relevant information, automate routine tasks and support decision-making across the care continuum. Rather than serving as a stand-alone application, AI becomes part of the infrastructure that helps health-care organizations operate more effectively.

This shift is particularly important as health systems work to address workforce challenges.

Many health-care professionals face growing administrative burdens and increasing complexity. According to the Centers for Disease Control and Prevention (CDC), nearly 46 percent of health workers reported feeling burned out often or very often in 2022.¹ While AI alone will not solve staffing shortages, it can help reduce friction in daily workflows, support consistency across care settings and allow clinicians to focus more of their time on activities that require uniquely human expertise.

What Defines Successful AI Adoption Within Enterprise Workflows in Health Care?

Importantly, successful AI adoption requires more than technology.

The most effective innovation happens when clinicians, operational leaders, technology teams and health-care organizations work together to solve real-world challenges. AI implementation must be grounded in clinical workflows, organizational priorities and patient needs, not technology for technology's sake.

Success depends on governance, transparency, interoperability, clinical engagement and trust. Organizations that begin with clearly defined operational and clinical challenges are often best positioned to realize value. Rather than asking where AI can be deployed, they ask where care delivery can be improved.

Health-care leaders do not need to ask where they can deploy AI. They should ask where care delivery is breaking down, where workflows create friction and where clinicians are spending time on tasks that technology can help simplify.

The organizations making the greatest progress are not starting with AI. They are starting with operational and clinical challenges, and using AI as one tool to solve them. The future of health care will not be defined by algorithms alone. It will be defined by how effectively organizations connect people, data, workflows and technology to deliver better care.

The next chapter of health-care AI will not be defined by the number of algorithms deployed. It will be defined by how effectively health-care organizations move from fragmentation to orchestration.

Reference

1. Nigam JA, Barker RM, Cunningham TR, Swanson NG, Chosewood LC. Vital signs: health worker-perceived working conditions and symptoms of poor mental health — quality of worklife survey, United States, 2018-2022. MMWR Morb Mortal Wkly Rep. 2023;72:1197-1205.