AI advances in radiology: the benefits for value-based primary care

Artificial intelligence, with its promise of efficiency and precision, has found its stride within radiology. Last year, radiology accounted for 87% of newly approved AI/ML devices, and 79% in the first half of this year. But what does this uptake in radiology mean for the rest of health care, including primary care?

The insights are clear: A recent report found that 60% of clinicians said advanced technology is becoming increasingly necessary to automate and streamline basic tasks. Yet nearly half find current medical technology needing more ease and intuitiveness. AI's integration into health care bridges this disconnect.

Clinicians are starting to see AI as an indispensable ally. With 61% acknowledging AI's support in clinical decision-making, it's not merely a futuristic promise—it's a present-day reality. However, there remains a palpable hesitation: 55% of clinicians feel AI technology isn't ready for medical application, and 58% distrust AI data.

This skepticism is not unfounded. It springs from a history of technology that sometimes seemed more an encumbrance than an aid. Information technology departments have a history of rolling out high-tech solutions without proper training or end-user buy-in. Moreover, AI algorithms, without the appropriate parameters and oversight, can develop biases that widen gaps in care.

Yet here lies the opportunity. As developers and health care providers, we must ensure AI is as understandable and transparent as a chest X-ray. When AI becomes another tool in the doctor's bag, vetted for accuracy and fairness, it strengthens the trust in AI across all health care sectors, including primary care.

AI's most exciting use cases in radiology involve delivering safer care and greater accuracy. That means the availability of radiology technology—especially when it’s portable and modular—can extend beyond the traditional radiology suite to include primary care.

Primary care physicians, who often face the challenge of providing comprehensive care with limited resources, can significantly benefit from these advances. By integrating AI-enabled imaging, PCPs can perform more diagnostic procedures in-house, reducing the need for referrals and providing immediate insights into a patient's condition.

This capability aligns perfectly with the principles of value-based care, which emphasizes patient outcomes and cost efficiency. By leveraging AI in imaging and beyond, PCPs can make more accurate diagnoses and initiate appropriate treatment plans quickly. This approach improves patient satisfaction by providing swift answers and decreases the likelihood of complications due to delayed diagnosis.

Furthermore, AI algorithms are continuously improving their diagnostic accuracy, which can help alleviate some of the skepticism PCPs may have. By starting with AI tools that have apparent impacts on safety and accuracy—while still leaving ultimately diagnostics to the doctor—health systems can create on-ramps to AI that break down barriers and build trust.

Finally, AI can help address the issue of health disparities. AI-enabled tools that can be used anywhere and operated by a broader range of practitioners can help doctors better serve rural or underserved areas, bringing advanced diagnostic tools to populations with limited access to such technology.

As AI technology advances and becomes more integrated into health care, its potential to assist PCPs in delivering value-based care is immense. It can help overcome skepticism by demonstrating tangible improvements in patient care and empower PCPs to be at the forefront of health care innovation. The promise of AI in health care is not just a boon for efficiency and accuracy; it's a step toward a more equitable, effective, and patient-centered health care system.

Evan Ruff is chief executive officer and co-founder of OXOS Medical