Artificial intelligence-powered medical technology is rapidly expanding across the health care industry. This includes Northeast Ohio, where hospitals are increasingly partnering with tech companies to improve patient care through customized AI workflows.
Though most widely used in radiology, AI is being adopted by hospitals for everything from summarizing reports to identifying stroke patients in emergency rooms more quickly.
When it comes to implementing AI at hospitals, Tom Valent, chief business and marketing officer for Aidoc, a health care technology company, said it can’t be one-size-fits-all.
“What we’ve learned in health care, at least in the U.S., is every hospital, and sometimes even within a hospital, is kind of its own unique creature,” Valent said.
He said the physician workflow of each technology is completely configurable to the specific hospital setting.
“The AI is the intelligence, but the real impact is the actual workflow solution, meaning being inside of how physicians normally practice medicine and making that better,” Valent said.
How Northeast Ohio hospitals are using AI
Building AI algorithms in recent years for health care spaces has become a swifter process. Where it used to take years to develop a single algorithm, or a set of rules to perform a specific function, companies and researchers can now create one in a matter of months.
AI algorithms “are the base of intelligent systems, which make it possible for machines to learn from the data, decide, and solve problems,” according to the Artificial Intelligence Board of America.
As of February 2026, the Food and Drug Administration approved more than 1,300 artificial intelligence-enabled medical devices and programs.
At Summa Health, Dr. Brian Bauman, head of pulmonary services and a pulmonary and critical care physician, said the hospital uses these AI programs:
- Nuance: A natural language software that picks up on key words in a scan report to prevent patients who need follow-up from being overlooked.
- Aidoc: A suite of more than 35 algorithms, including those for diagnostic radiology, acute care coordination and patient navigation.
At University Hospitals, Dr. Leonardo Kayat Bittencourt, vice chair of innovation, said they use Aidoc along with these five programs:
- Riverain Technologies: This company’s algorithm, ClearRead CT, is used for automated detection and characterization of lung nodules on CT scans.
- Qure.ai: A suite of applications includes tools that identify at-risk patients for certain diseases based on imaging.
- Heartflow: Creates a 3D model of the heart that can be analyzed and measured.
- NeuroQuant: Measures brain issues on MRIs, developed by Cortechs.ai.
- RapidAI: Tool offers automated detection and communication of strokes for patients serviced in the emergency department with symptoms.
At the Cleveland Clinic, Dr. Po-Hao Chen, vice chair for artificial intelligence in the Diagnostics Institute, said they use direct decision support, indirect clinical support and image generation programs. Along with Riverain Technologies, Cleveland Clinic uses these AI programs:
- Viz.ai: Identifies patients who are at highest risk for stroke. Used for triage.
- RadAI: Helps automate reporting and create concise conclusions in radiology.
- iCAD: Outlines where a suspicious mass is located. Part of DeepHealth.
- Image generation programs: Can accelerate image reconstruction from MRIs.
The Cleveland Clinic regional hospital system includes Akron General, Mercy and Medina hospitals.
Aidoc, a one-stop shop for medical AI
Aidoc, which Valent said has become a one-stop-shop platform for multiple algorithms, works through an AI operating system.
Valent said it’s essentially the connective tissue between a hospital’s existing IT and the algorithms. The operating system can identify the patient and the type of scan to be read, and it activates the correct algorithm on its own.
Then, it triggers a specific customized workflow setup for that hospital and physician.
Aidoc has three product lines:
- Diagnostic radiology: Helps radiologists view scans. Flags the most urgent patients, increasing efficiency.
- Acute care coordination: Streamlines care and alerts doctors when they’re needed. Decreases time to treatment and ensures patients who need follow-up don’t fall through the cracks.
- Patient navigation solution: Identifies patients as abnormal and alerts physicians.
Bittencourt, who also works in abdominal imaging, said University Hospitals uses just over 10 of Aidoc’s algorithms.
“[Aidoc] is kind of an alarm or an alert that pops up whenever we see a case or whenever we are covering a certain list that then calls the attention of the radiologist to ultimately give their own adjudication and assessment to the exam,” he said.
Valent said more than 1,600 hospitals are using Aidoc, and they cover almost 50% of patients in Ohio.
ClearRead CT used to read radiology scans
It’s becoming more common for hospitals to use AI when reading radiology scans.
Studies have shown that AI can meet or exceed the performance of human experts in image-based diagnoses from several medical specialties.
Steve Worrell, CEO of Riverain Technologies, which created ClearRead CT, said the algorithm can read a patient scan in about 4 minutes and identify lung nodules that may be cancerous.
“Because radiologists are more and more burdened with more data, the potential for oversight is greater,” he said. “So, we want to improve their accuracy, but also improve their reading efficiency.”
When a patient gets a scan using ClearRead CT, the data is routed simultaneously to the picture archiving and communication system and the algorithm.
ClearRead analyzes individual slices of a CT scan. Its results are pushed to the archiving system, and when the radiologist opens a patient file, both the original scan and ClearRead’s content are available to them.
“We are doing our processing before the radiologist ever even thinks about opening that patient file,” Worrell said.
The radiologist is able to do a concurrent read of the scan while seeing ClearRead’s results at the same time, double-checking areas the algorithm flags, he said.
Worrell said roughly 250 sites nationwide are using ClearRead CT.
AI implementation and training
Chen, who is also a diagnostic radiologist, said when Cleveland Clinic implements a new type of AI, it’s important that physicians are trained on its intended use and intended users.
“You need to know what the AI is not going to do for you — that’s still on you,” he said.
Everyone also needs to be trained to be part of the developed workflow. Medical care doesn’t happen with just one doctor — it takes entire teams, Chen said.
Worrell said Riverain has its own virtual training that helps doctors understand ClearRead’s limitations and sources of false positives.
“You can’t trust a ‘no’ flag, meaning a negative result on the AI, for what it is,” Chen said.
Building and validating AI tools
Bittencourt said University Hospitals is part of a consortium created by Bunkerhill Health that makes getting algorithms into clinical practice easier.
Nishith Khandwala, CEO and co-founder of Bunkerhill, said the company’s goal is to bring more AI into the health system to help hospitals increase efficiency and deliver better care.
“We want to bring every idea that you have that could help patients, that could help health systems, that could help the health care ecosystem more broadly, to the clinic as quickly as possible,” Khandwala said.
He said it can be difficult to get algorithms into clinical practice after they’ve been built.
Because they’re often created using one hospital’s data, the chances of the AI working at other hospitals are low because every patient population looks different — and he said it can take years to get data from other hospitals.
Bunkerhill’s consortium involves 27 academic medical centers and creates a “legal bubble” where researchers from one health system can share data and algorithms with researchers at other health systems.
“A project that might have lost momentum otherwise, or a project that would have taken two or three years to go from start to finish, can potentially be compressed in a matter of months,” he said. “You can go from idea to something that has been tested on data from multiple hospitals many times.”
Internally, Cleveland Clinic has an overarching AI task force that oversees approval of AI algorithms before they go into clinical practice, Chen said.
University Hospitals has a “hardcore group” of AI experts who are radiologists in different subspecialties who analyze new AI tools together, Bittencourt said.
Bittencourt said no AI algorithm runs autonomously or in a way that replaces a doctor’s judgment.
“The algorithm and AI are just a piece,” Valent said. “The other pieces are as important.”
Lauren Cohen is a community reporting intern for the Akron Beacon Journal and Signal Akron. The position is funded through a grant from the Knight Foundation.