Making AI diagnostics work in the real world: A practical guide for primary care physicians

Artificial intelligence has arrived in medicine with both considerable fanfare and confusion. For primary care physicians already stretched thin, the promise of AI-assisted diagnostics can sometimes feel like one more thing to evaluate, budget for and explain to patients. Yet some of these tools are already transforming what's possible in everyday practice: enabling earlier detection of serious disease and helping more patients get the right care without unnecessary delays. The key is knowing how to evaluate them.

Why strong technology doesn't always mean easy adoption

Many AI diagnostic tools demonstrate impressive technical performance in clinical studies, but robust evidence alone does not translate into routine clinical integration. The barriers to adoption tend to cluster around three core challenges: clinical evidence and guideline support, reimbursement, and organizational readiness.

Clinical evidence comes first. A new tool needs to demonstrate that it performs at least as well as, or preferably better than, existing alternatives. This evidence base also serves as the foundation for guideline inclusion, which signals to physicians and health systems that a tool merits adoption. Technology is not adopted for its own sake. Routine utilization generally depends on improvements in clinical care to catalyze change. It takes many years for a new technology to enter clinical guidelines. Staying curious and informed by conducting your own due diligence is prudent, professional and may be essential to ensure patients have access to the best possible care

Reimbursement is a key determinant and enabler of routine adoption. Physicians strive to provide the best care possible, but doing so is challenging when a tool generates unsustainable costs. When an AI diagnostic lacks coverage from CMS or commercial payers, the expense of deploying it at scale may be untenable, even if the technology performs beautifully. This reality may lead to tough decisions. When reimbursement is established, the decision to adopt becomes much easier.

The third barrier is organizational. Physicians require appropriate infrastructure and institutional support to integrate new tools into practice securely and efficiently. This is both a technological and cultural challenge, but one that practices have more direct control over than the first two. Additionally, technologies must fit within a clinician’s existing workflow, otherwise they will face more substantial barriers to adoption.

Understanding the reimbursement landscape

For any AI diagnostic tool, reimbursement hinges on three things: a billing code, payer coverage and an established payment rate. Many AI tools on the market today have a Category III CPT code — a temporary designation used during the evidence-building phase — but lack active coverage from Medicare or commercial insurers. This means that while a code exists on paper, payments may be inconsistently issued, vary by payer and require frequent appeals.

Eventually, a Category III code may meet the criteria for conversion to a permanent Category I code, with widespread payer coverage and a defined payment rate. Relatively few AI diagnostic tools have reached this level, but those that do represent a fundamentally different financial proposition for a practice. When evaluating any AI tool, it is important to consider not only whether a billing code exists, but also the broader reality of the reimbursement landscape.

Concierge and cash-pay practices occupy a different position altogether: reimbursement is less of a constraint, and patients in these settings often expect access to newer technologies. This gives concierge physicians the flexibility to evaluate promising tools before they've achieved broad payer coverage.

Why algorithmic transparency matters

One of the most important questions to ask about any AI diagnostic tool is simply: "Can I reasonably understand how this works?" To trust a tool and use it effectively, physicians need a working understanding of what it is actually doing. This does not mean understanding the source code, but it does mean knowing enough to interpret outputs accurately, recognize when a result might be unreliable and explain findings to a patient in a succinct and understandable way.

Tools that function as “black boxes” create real problems, both clinically and in the exam room. AI output should never be the sole basis for a clinical decision. It is one input among many, and using it appropriately depends on understanding its strengths and limitations. A physician who cannot explain how a tool reached its conclusion is in a difficult position when a patient asks, "Why do I need this procedure?”

Fitting new tools into a busy practice

The most sustainable AI tools are the ones that integrate into existing workflows rather than requiring physicians to restructure their practice around them. When evaluating a tool, think about how it fits into the sequence of care. Does it require a separate platform? Does it generate reports that integrate with your EHR? Does it add steps to the visit, or does it quietly enhance what you're already doing? The less friction a tool introduces, the more likely it is to become a sustainable part of routine care.

A closer look: Preventive cardiovascular diagnostics

One area where AI diagnostics are showing particularly strong promise is cardiovascular disease prevention. Heart disease remains the leading cause of death in the United States, and approximately 80 to 90% of heart attacks are caused by plaque rupture in the coronary arteries.

Coronary CT angiography (CCTA) has emerged as an important tool in this effort. Updated guidelines now designate CCTA as the only Class IA diagnostic for stable chest pain, the highest level of evidence and recommendation. Multiple studies have shown it to be more accurate than traditional stress testing for identifying coronary artery disease.

For cardiologists, AI tools that analyze CCTA data, such as coronary plaque analysis and fractional flow reserve derived from CT (FFR-CT), provide detailed quantitative information about plaque composition and hemodynamic significance on each vessel and segment of coronary arteries. But the relevance extends to primary care as well. Automated plaque analysis produces a color-coded, quantified output that doesn't require specialized cardiology training to interpret. A primary care physician can review this information, alongside a reading physician as needed, then use it to guide conversations about medication and lifestyle, educate patients with clear visualization of their cardiovascular disease and make more informed decisions about when a cardiology referral is actually warranted. Research has shown that patients who can visually see their disease burden are significantly more likely to follow through on treatment recommendations, including taking daily medications. For patients likely to resist intervention until they've experienced a serious event, that tangible evidence can be a turning point. These tools also fit directly within the existing cardiovascular workflow.

Staying current without drowning

Primary care physicians are expected to stay current across an enormous range of clinical domains. Adding AI diagnostics to that list can feel overwhelming. Conferences and CME programs increasingly include sessions on digital health and AI. Peer-reviewed publications and Google Scholar alerts tied to your areas of interest can surface evidence as it emerges. Newsletters from professional societies often highlight tools that are gaining traction.

It would not be possible to efficiently evaluate every AI tool on the market. A useful shortcut may be to begin with tools that already have Category I reimbursement and established clinical evidence since these have cleared the highest bars for both clinical validity and financial sustainability. This approach will narrow the field considerably.

The bottom line

AI diagnostics are not a single category. They vary enormously in clinical rigor, reimbursement status, transparency and ease of integration. For primary care physicians, the most important question isn't whether to engage with these tools, but how to evaluate them wisely.

Done thoughtfully, integrating the right AI diagnostics into primary care can extend what is possible within your practice by identifying serious disease earlier, reducing unnecessary referrals, and giving patients a clearer view of their own health. That's not a technology promise; for the tools that have done the work both clinically and economically it's an increasingly routine reality.

Blake Richards is COO of Elucid, a Boston-based medical technology company dedicated to bringing precision patient care, as currently practiced in oncology, to cardiology. He has extensive healthcare technology experience, including leading revenue cycle and patient confidentiality for Epic Systems, as a life sciences strategy management consultant for Deloitte, and in strategy for several imaging- and AI-driven medical technology start-ups.