Transparency is vital for AI usage in health care, patient-provider relationship, OHIO researchers find

Among the top artificial intelligence companies, the current race is ultimately to build better, faster and more accurate algorithms. Artificial intelligence (AI) has become commonplace in every sector including health care, and an increasing number of primary care physicians are turning to AI for everything from diagnoses to patient note transcriptions.

Even with all of the useful applications artificial intelligence has as a health care tool, many patients are still skeptical of its use. A 2023 Pew Research study revealed that 57% of respondents believed AI in health care would negatively impact the patient-provider relationship, mainly due to mistrust stemming from AI's lack of social presence, black-box nature, bias and opacity.

As AI continues to reshape the health care landscape and become more prominent at health care facilities, three Ohio University researchers have found that addressing transparency concerns is actually the most critical way to foster trust in primary care providers and improve patient outcomes.

Ohio University Professors of Analytics and Information Systems Gaurav Bansal, Ph.D. and Vic Matta Ph.D., along with recent OHIO alumnus Kevin Diaz-Ordonez, began investigating how AI in health care may impact the patient-provider relationship among primary care physicians in fall of 2024. Their research mainly focused on comparing the relative importance of AI accuracy and transparency in health care settings. 

Bansal said their objective was to understand which factor—accuracy or transparency—is more important and how it impacts trust in a primary care providers in a diagnostic setting. 

“This research intends to define transparency in the context of health care and AI use,” said Bansal. “It is not about the transparency of the AI algorithm and how it was developed and how it works. It is the transparency of the process, and how AI is being used in a health care clinic. For example, a doctor discussing how they are using AI, or how much they rely on AI for a diagnosis.”

Bansal, Matta and Diaz-Ordonez hypothesized that transparency would lead to trust in the health care provider and trust in any AI being used by the provider—the same logic as a patient trusting a prescription given to them due to their trust in their physician who prescribed it. This hypothesis was grounded in medical literature.

“We argue that trust is what leads to positive attitudes towards the use of AI by the health care provider, and that leads to satisfaction in the healthcare process,” said Bansal. “Satisfaction has been argued to be the key construct in health care settings, because if patients are not satisfied with their health care settings that could lead to adverse outcomes. So, satisfaction, trust and attitude are all important here.”

They also examined how accuracy impacts these relationships. Accuracy was used as a moderating role in relation to transparency. Their assumption was that if people trust a health care professional, then they will trust that physician’s AI usage. And, if the AI that is being used is accurate, the patient will trust the physician and their AI usage even more.

The results of their study are where things really get interesting.

To examine their hypothesis, the three investigators enlisted respondents to participate in a scenario-based survey experiment on Mechanical Turk (MTurk), a crowdsourcing marketplace. Data from 655 MTurk respondents was used to gather a sample. Bansal, Matta and Diaz-Ordonez used attention checks and asked follow-up questions to ensure their data was reliable and valid.

As anticipated, transparency was found to be vital to the patient-provider relationship in relation to AI usage, and thus their key hypothesis was supported. Transparency in AI usage was important, and it led to higher trust in the health care provider who used it, as well as higher trust in the AI as used by their doctors. Essentially, the researchers found it to be true that if you trust the provider, then you trust the tools they use. 

What was more surprising was the reaction to the role of accuracy in AI diagnoses and the impact on the patient-provider relationship. When transparency increased, trust increased—as expected, but when AI was more accurate, trust actually went down or stagnated. Bansal has a few potential explanations for why this is the case.

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“People are afraid that if AI becomes too accurate, doctors might not use their own critical judgment and it will be outsourced to AI,” he said. “Especially primary care. I think that fear is being captured here.”

Matta said these findings have the potential to change the whole game in terms of how we think about the relationship between artificial intelligence and trust.

“The reason that this is not just a big deal, but a huge deal is because it’s contrary to beliefs that accuracy improves trust,” said Matta. “The implications are massive. Without this discovery, we would all be afraid that doctors are going to be replaced by AI, but because of this study we can say ‘not so fast.’”

These findings were presented at the May 2025 Midwest Association for Information Systems (MWAIS) Conference in Oklahoma. This year’s MWAIS 2026 conference was held at Ohio University. The study is currently available through a conference proceeding, and Bansal and Matta are working on making it a journal publication, as well.

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The results are being corroborated. Matta and Bansal have seen many recent papers argue that AI-generated outputs are less valuable than those that are human-generated. Bansal agrees that these results essential reshape the common understanding of how accuracy in AI is perceived but says how these findings are interpreted is very important.

“We have to be careful how we interpret these findings because they’re in the context of transparency and accuracy,” Bansal explained. “We should not generalize that accuracy is not important in AI health care. In the context of transparency and accuracy, transparency is more important, and if transparency is present, accuracy plays a lesser role.”

Bansal and Matta also want to make it clear that just because accuracy in AI wasn’t important in primary care contexts, doesn’t mean it won’t be important in other health care settings. They are hoping to expand this research to specialized care to compare these findings to another health care area. 

“One study is never enough,” said Bansal. “We have found something counter-intuitive that means we need to do more research.”

This research on the impact of AI health care tools on patient-provider relationships and satisfaction of care is a prime example of collaborative research environment that exists at Ohio University. 

The undertaking itself began as Diaz-Ordonez’s undergraduate project. Bansal and Matta said that the  OHIO’s Discover Research Grant helped them to engage Diaz-Ordonez, who was then an undergraduate student majoring in Management Information Systems. The Discover Research Grant is part of OHIO’s Dynamic Strategy under the Discover Pillar, and it supports travel to conferences in order to present valuable research.

“Our Discover Grant was designed to promote research that aligns with our University Discover Pillar,” said Bansal. “Health care and healthy aging are one of those focus areas. This was very helpful in deciding what domain to choose. We definitely want to attribute the health care inspiration to that grant.”

If there is one thing to take away from this study, Bansal hopes it is the realization that transparency is extremely important in the realm of artificial intelligence—and especially as it relates to AI use in health care environments. He insists that we must have more discussions about transparency in our society.

“AI will never be fully accurate,” Bansal explained. “There will always be an accuracy problem because it is built on historical data, but you can perfect transparency in your processes.”

Bansal and Matta were both surprised at how little literature and research existed about the importance of transparent AI usage in general. 

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“All the big AI companies are focused on algorithms, speed, building big data centers, making the AI faster,” said Bansal “You can do all that, but our expertise in the college of business are processes and governance. We are advocating for better processes and transparency. This is one area that nobody is talking about. Everyone is focused on this mad race to build bigger and better AI algorithms. Our research is saying to focus on processes, governance and transparency. Don’t forget them.”

Bansal further emphasized this point when he grabbed a jacket in his office and read the size, material and washing instruction information off the tag. 

“There is more transparency in a $20 piece of clothing than AI,” he said.

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