By Sean C. Orr, M.D.
8-Minute Read
How Inspiring AI diagnostics are helping physicians
A confluence of technologies is poised to help us move beyond the traditional tradeoffs that have constrained medicine for decades. Among these, artificial intelligence in diagnostics represents perhaps the most immediate and tangible opportunity for practicing physicians in Florida and the nation.
But the opportunity comes with a warning that every physician should internalize before adopting any new tool: technology must serve the physician-patient relationship, not replace it. The moment AI becomes a substitute for clinical judgment rather than an augmentation of it, we have traded one form of institutional capture for another.
The Diagnostic Landscape in 2026
The diagnostic process in medicine has always been an exercise in pattern recognition under conditions of uncertainty. A patient presents with symptoms. The physician synthesizes history, examination findings, and laboratory or imaging data into a differential diagnosis. The quality of this process depends on the physician’s training, experience, cognitive bandwidth, and the time available for each encounter.
Each of these variables is under pressure. The administrative burden on physicians has expanded relentlessly. Electronic health records, which were marketed as tools for clinical efficiency, have in practice become documentation engines optimized for billing compliance rather than diagnostic reasoning. The average physician now spends two hours on EHR tasks for every hour of direct patient contact. This is not a technology problem. It is a misalignment problem. The technology was designed to serve the payer-regulatory complex, not the physician or the patient.
AI-driven diagnostic tools offer a potential correction to this misalignment, provided they are designed and deployed in ways that genuinely augment clinical reasoning rather than adding another layer of administrative complexity. The five tools discussed here represent the current state of this rapidly evolving field, each with distinct applications relevant to Florida physicians.
Tool 1: PathAI and Computational Pathology
Pathology has always been a visual discipline, dependent on the pattern recognition abilities of trained pathologists examining tissue specimens under microscopy. PathAI and similar computational pathology platforms apply deep learning algorithms to digitized slide images, identifying patterns that may be subtle or inconsistent across human observers.
For Florida physicians, the relevance is both clinical and structural. Florida’s pathology workforce, like its physician workforce generally, faces significant shortage pressures, particularly in rural and underserved areas. A computational pathology system that can provide rapid preliminary assessments does not replace the pathologist. It extends the pathologist’s reach, allowing a single specialist to oversee a larger volume of cases with greater consistency.
The critical question, as with all AI tools, is who controls the output and how it integrates into clinical decision-making. If computational pathology results flow directly into treatment algorithms without physician oversight, we have simply automated one bottleneck while creating another. If, instead, the tool functions as a second opinion that enhances the pathologist’s confidence in their assessment, we have genuinely added value. Value, as I have articulated elsewhere, is the quotient of quality of care divided by cost of care. The numerator must always be the priority, but simultaneously reducing cost delivers outsized value to the marketplace.
Tool 2: Viz.ai and Stroke Detection
As a neurologist, this tool is particularly relevant to my practice. Viz.ai uses AI algorithms to analyze CT angiography images in real time, identifying large vessel occlusions that indicate acute ischemic stroke requiring emergent intervention. The system automatically alerts the stroke team, bypassing the traditional sequence of radiology read, physician notification, and team activation that can consume precious minutes in a time-sensitive emergency.
In Florida, where the stroke burden is disproportionately high due to our aging population and the geographic dispersion of neurology expertise, this technology addresses a genuine access problem. A patient presenting to a community emergency department in rural North Florida may be hours from the nearest comprehensive stroke center. If AI can identify the large vessel occlusion within minutes of the CT scan and trigger a transfer protocol immediately, the time savings translate directly into preserved brain tissue and improved outcomes.
The tool has demonstrated sensitivity above 90% for large vessel occlusion detection, with processing times measured in minutes rather than the 30 to 60 minutes that traditional notification chains can require. For the practicing neurologist, this means earlier involvement in treatment decisions and better outcomes for patients who would otherwise face delays.
But I would offer a caution rooted in experience. The value of Viz.ai depends entirely on the infrastructure that surrounds it. An AI alert is useless if the receiving hospital lacks the interventional capability to act on it, if transfer protocols are bogged down in administrative approvals, or if the neurologist on call is managing an unsustainable patient load because the system has failed to invest in adequate staffing. Technology cannot compensate for institutional malalignment. It can only amplify whatever alignment or misalignment already exists.
Tool 3: IDx-DR (LumeneticsCore) for Diabetic Retinopathy
IDx-DR, now known as LumeneticsCore, became the first FDA-authorized autonomous AI diagnostic system in 2018, and its relevance has only grown as the diabetic population in Florida continues to expand. The system analyzes retinal images captured by standard fundus cameras and provides an autonomous diagnosis of diabetic retinopathy without requiring specialist interpretation.
For Florida’s primary care physicians, particularly those serving large diabetic populations in medically underserved areas, this tool addresses a critical screening gap. The American Diabetes Association recommends annual retinal screening for all diabetic patients, yet compliance rates remain below 60% nationally. The barrier is not patient willingness. It is access to ophthalmologic evaluation, particularly in communities where the nearest retinal specialist may be hours away.
LumeneticsCore allows the screening to occur in the primary care office during a routine visit. The fundus camera captures the images, the AI provides the assessment, and the physician can counsel the patient immediately on the findings. Patients with positive screens are referred for specialist evaluation. Patients with negative screens receive reassurance and a documented baseline.
From a market perspective, this is a tool that genuinely reduces the cost of screening while maintaining or improving quality. It does not require the physician to surrender clinical judgment. It extends the physician’s diagnostic capability into a domain where most generalists lack the specialized training to render independent assessments. This is technology in service of the physician-patient relationship, not in competition with it.
Tool 4: Tempus and Genomic-Driven Oncology
Tempus has built a platform that integrates genomic sequencing, clinical data, and AI-driven analytics to support precision oncology decision-making. For Florida oncologists, the platform offers tumor profiling that identifies actionable mutations, predicts treatment response, and matches patients with relevant clinical trials.
The relevance to physicians extends beyond oncology. The Tempus model represents a template for how AI can be deployed in the service of personalized medicine at scale. As I discussed in “Rebuilding Medicine,” personalized medicine in its early iterations was laborious, time-consuming, and expensive. The promise of AI-assisted platforms like Tempus is that they can reduce the labor and cost while increasing the precision.
The caution here involves data ownership and transparency. Tempus and similar platforms accumulate enormous datasets from the patients and physicians who use them. The question of who owns that data, who profits from its secondary use, and whether the physician and patient have meaningful control over its disposition is not a technical question. It is an ethical and economic question that goes to the heart of the physician’s role as a fiduciary for the patient. Any platform that extracts data from the physician-patient relationship and monetizes it without transparent consent and equitable compensation is engaging in the same kind of intermediary rent-seeking that characterizes the insurance and hospital administration industries.
Tool 5: Aidoc and Radiology Triage
Aidoc provides AI-powered radiology triage, analyzing CT scans to identify critical findings such as pulmonary embolism, intracranial hemorrhage, cervical spine fractures, and aortic emergencies. The system flags urgent cases and pushes them to the top of the radiologist’s worklist, ensuring that time-sensitive findings receive immediate attention.
In the Florida context, where emergency department volumes are high and radiology staffing is often stretched thin, this kind of intelligent triage addresses a real clinical need. A pulmonary embolism identified and flagged within minutes of the scan rather than waiting in a routine reading queue can mean the difference between timely anticoagulation and hemodynamic collapse.
The broader lesson from Aidoc is that the most valuable AI applications in medicine are not those that replace physician judgment but those that optimize the allocation of physician attention. The radiologist still reads every scan. The AI simply ensures that the most critical scans are read first. This is a model of technology deployment that respects professional autonomy while acknowledging the reality of resource constraints.
The Physician’s Role in the AI Transition
The five tools described here share a common characteristic: each augments the physician’s diagnostic capability without removing the physician from the decision-making process. This is the standard by which all AI applications in medicine should be evaluated. The question is not whether AI can perform a specific diagnostic task. In many narrow domains, it already can. The question is whether the deployment model preserves the physician’s role as the patient’s advocate, fiduciary, and primary decision-maker.
The assassination of United Health Care CEO Brian Thompson and the public outrage it catalyzed should remind us that patients do not trust algorithms. They do not trust corporations. They trust their physicians, or at least they want to. When we deploy AI in ways that maintain and strengthen that trust, we are building toward the metamodern vision of medicine I have described: one that integrates the best of technological capability with the irreplaceable human elements of empathy, judgment, and the physician’s sacred obligation to the individual patient.
When we deploy AI in ways that distance the physician from the patient, that automate care decisions without physician oversight, or that extract data from the clinical encounter for corporate profit, we are simply replacing one set of intermediaries with another. The insurance company that denies claims using AI is not fundamentally different from the hospital system that uses AI to reduce physician headcount. Both represent the subordination of the physician-patient relationship to institutional interests.
Practical Considerations for Physicians
Evaluate AI diagnostic tools through the lens of value: Does this tool improve the quality of care I deliver, and does it do so at a cost that is justified by the improvement? If the answer to both questions is yes, the tool deserves serious consideration.
Insist on transparency in how AI tools process data and generate recommendations. A tool that functions as a black box, providing outputs without intelligible explanations, is not a clinical decision support system. It is an oracle, and physicians do not practice medicine by consulting oracles.
Maintain ownership of the clinical decision. No AI output should override or circumvent physician judgment. The tool recommends. The physician decides. The patient consents. This chain of authority is not a relic of a pre-technological era. It is the foundation of ethical medical practice.
Advocate for regulatory frameworks that promote innovation while protecting the physician-patient relationship. The current regulatory environment, dominated by the FDA’s cautious approach to AI clearance, creates barriers to entry that favor large corporations over physician-led innovation. A more rational framework would establish clear standards for safety and efficacy while allowing the market to determine which tools provide genuine value.
The future of diagnostics in Florida medicine is bright, not because of the technology itself, but because the technology, properly deployed and aligned, can help physicians reclaim the time and cognitive bandwidth that decades of administrative encroachment have stolen. That reclamation is the essential precondition for everything else we hope to achieve: Better care, better practice, better life.