cliexaAI: From Reactive Healthcare to Proactive Clinical Intelligence

Mehmet Kazgan spent nine years building an AI platform that does what general-purpose tools cannot: connect patient data to clinical reasoning in real time. The results are starting to speak for themselves.

Mehmet Kazgan, founder and CEO of cliexa, recently sat down with Avrohom Gottheil on the #AskTheCEO podcast to discuss what is missing in today's healthcare AI landscape, how cliexaAI fills that gap, and what the platform's Mayo Clinic Platform qualification means for the future of clinical decision support.

Healthcare has adopted AI faster than almost any other industry. AI scribes transcribe conversations into clinical notes. Interoperability platforms connect data across systems. Generative models answer questions and summarize patient records. The technology is impressive, and the adoption is accelerating.

But according to Kazgan, there is a critical layer missing from most of these tools: clinical reasoning.

"Getting something done in healthcare is not really getting you paid," Kazgan explains. "Without the reasoning built into that work, whether it's an AI scribe or interoperability, you're not going to get the outcomes you're looking for. That's what's missing today — the clinical reasoning."

That gap is what cliexaAI was built to fill. And after nine years of training, partnerships with the American College of Cardiology and Mayo Clinic Platform, and over 12 million real patient data points, the platform is producing results that are difficult to ignore.

Why Clinical Reasoning Matters

The distinction Kazgan draws is between tools that automate tasks and systems that understand clinical context. An AI scribe can turn a conversation into a note. A clinical reasoning engine can assess whether the treatment documented in that note aligns with the patient's comorbidities, insurance guidelines, and historical risk factors — and flag when it doesn't.

"If you're implementing an AI scribe, yes, it transcribes, it creates convenience for the clinician," Kazgan says. "But does it really get the best patient outcomes? And are the hospital systems getting paid? Because if you're not getting paid, that convenience doesn't mean a lot."

cliexaAI addresses this by combining predictive modeling with generative AI in a dual large language model architecture. The predictive layer recognizes patterns across multiple comorbidities — how a patient's depression might affect their cardiovascular condition, or how a diabetes diagnosis interacts with chronic kidney disease progression. The generative layer applies clinical and insurance guidelines in real time, personalizing recommendations at the point of care.

The Opioid Use Disorder Challenge

The platform's first major qualification milestone came through its opioid use disorder (OUD) prediction solution. The problem is well understood: opioid prescriptions can lead to addiction and overdose, and clinicians have limited time during each patient visit to identify early risk indicators.

"You have a limited time of seeing the patient, limited time of looking at their information, aggregating it and synthesizing it and making a decision," Kazgan explains. "It's very hard for the clinician to identify a risk for the patient at that point."

cliexaAI paints a risk picture for the provider by analyzing the patient's full data profile — including comorbidities, medication interactions, demographics, and historical patterns from the facility's own patient population — and presenting a risk prediction alongside the specific factors driving that prediction. The clinician still makes the decision. The AI provides the intelligence to inform it.

Why Custom Models Outperform General-Purpose AI

Kazgan draws a sharp line between cliexaAI's approach and the wave of AI wrappers built on top of publicly trained models like those from OpenAI and Anthropic.

"We need to remember these solutions have been trained with public models. Their job is to generate," he says. "The model you train somewhere else doesn't necessarily mean it's going to give the same outputs in your use case, because the statistical information is going to change."

He illustrates the difference with an analogy: imagine a baby playing in a room. One approach to safety is constantly telling the baby not to touch the power outlet on the wall — that's the restrictive modeling approach used by general-purpose AI, where the system knows everything and needs guardrails to limit its outputs. cliexaAI takes the opposite approach.

"Our custom model doesn't even know the power outlet exists on the wall," Kazgan explains. "We're limiting the factor by not introducing it to the model, but benchmarking information within a closed-loop system where we've already trained and can quickly calibrate to that patient data."

The distinction matters in healthcare because bias and hallucination carry clinical consequences. A publicly trained model that generates a statistically reasonable but clinically inaccurate recommendation could affect patient care. cliexaAI's closed-loop approach, trained on 12 million real patient data points over nine years, calibrates to each facility's specific patient population and clinical protocols.

Mayo Clinic Platform Qualification

The milestone that validates this approach is cliexaAI's qualification on Mayo Clinic Platform — a process that took three years from initial selection through the Accelerate Program to full qualification.

"We're one of the first four companies selected to that program. I think they're over a hundred now," Kazgan says. "We've been through a very detailed process of monitoring — surveys, questionnaires, a lot of matrices. They're not going to put any solution to white label and make available to their network without that validation."

The result: cliexaAI is the first and only clinical decision support platform for opioid use disorder in the Mayo Clinic Platform portfolio. The qualification opens distribution to Mayo Clinic's network and provides third-party validation that the platform's methodology, accuracy, and governance meet the standards of one of the most recognized healthcare institutions in the world.

How cliexaAI Works in Practice

The platform is designed to integrate into existing clinical workflows without requiring clinicians to learn a new system or change how they work.

"The solution is fully containerized and packaged and deployed on the customer side," Kazgan explains. "This intelligent brain deploys and then wires itself to data, starts calibrating itself. It's running behind the electronic medical records."

The prediction — high, medium, or low risk along with the contributing risk factors — appears within the clinician's existing EMR tools, either in a frame, a tab, or a browser screen accessible from within their current workflow. The platform integrates bi-directionally with over 13 EMR systems.

"We don't replace workflows, systems, or people. We optimize them," Kazgan says. "You still use your same systems, you still follow the same workflows, you still use the same people. We're just running behind the scenes, giving you the picture within your own tools."

Human in the Loop

For healthcare leaders concerned about trusting AI with clinical decisions, Kazgan describes a governance model where every AI output is reviewed before it becomes a clinical record.

"Before that system generates a record, you still need to have a human in the loop to monitor and approve what is being output," he explains. "If the clinician makes a correction, that creates a delta. The AI learns from it. But even putting that learning back into the system requires human approval."

This continuous cycle of output, review, correction, and approved learning is what Kazgan calls AI governance — the layer that builds trust over time by ensuring the system improves under clinical supervision rather than operating autonomously.

Results From Early Deployments

The platform's real-world impact is measured in clinical and financial outcomes from pilot deployments.

"If you're an entity and I tell you your denial rates can decline 35% to 50% within six months, that's a quick dollar return," Kazgan says. "That translates directly into what the outcome will be on the financial and revenue cycle side."

Nine Years of Building

cliexaAI's current capabilities are the result of a deliberate, decade-long development path. The company started in 2016 with smart intake — aggregating and synthesizing patient data at the point of care while applying payer logic so clinicians could understand what treatments would be covered.

Between 2018 and 2022, the platform expanded through a partnership with the American College of Cardiology, adding cardiovascular risk assessment to the multi-comorbidity model. Kazgan also started his own clinic to collect proprietary data and validate the opioid use disorder prediction model in a real clinical environment.

In 2022, cliexa entered the Mayo Clinic Platform Accelerate Program. Three years of training, validation, and governance work followed, resulting in the current qualification.

Today, cliexaAI extends beyond OUD into triage, ICU mortality risk prediction, chronic kidney disease progression, cardiovascular risk, and eating disorders. The company is growing from 23 to 27 employees, and a new government partnership is in the works.

Beyond Opioid Use Disorder

The platform's dual LLM architecture allows it to apply the same clinical reasoning approach across multiple disease areas. Kazgan describes a project with a pharmaceutical company where cliexaAI predicted the stages and speed of chronic kidney disease progression by understanding how hypertension, diabetes, and chemotherapy exposure interact in the same patient.

"We are combining cardiovascular risk with oncology risk together and saying these are the early indicators and multi-comorbidities that can increase the speed of the patient's progression," Kazgan explains. "That gives us a differentiator from other tools that focus on a single condition."

The eating disorders work is particularly notable. The third scientific paper, approved by the International Journal of Eating Disorders, demonstrates that cliexaAI can support early intervention weeks before clinical outcomes would typically present — a capability Kazgan believes may be one of the earliest applications of AI-powered clinical decision support in eating disorder treatment.

What Healthcare Leaders Should Pay Attention To

Kazgan's message to healthcare decision-makers is direct: look past the convenience features and evaluate whether the AI solution actually delivers clinical outcomes and gets the organization paid.

"Don't settle for convenience," he says. "Ask how the models are trained, which data sets were used, and what outcomes the solution can provide specifically for your organization. One review doesn't mean anything. Look at ten."

When asked to summarize cliexaAI in a single sentence, Kazgan describes it as "clinical intelligence that can understand clinical comorbidities at point of care and calibrate itself to that subspecialty immediately — giving the best optimal care outcomes for the patient and the provider."