Artificial intelligence is becoming an increasingly visible part of healthcare. From administrative workflows and clinical decision support for remote monitoring and health technologies, organizations are exploring how artificial intelligence can help them process information more efficiently and make health-related data more visible. However, as the rate of adoption increases, one challenge continues to affect whether these technologies can be meaningfully adopted.
Trust has become a central issue in the broader conversation around artificial intelligence. The Global Risks Report of the World Economic Forum announced the 2026 ranking disinformation and disinformation is the second most severe short-term global riskIn the long-term perspective of the report, concerns about the negative consequences of artificial intelligence technologies have increased significantly. As organizations introduce AI into increasingly sensitive areas, including healthcare, the findings underscore its importance transparencyin creating public trust, governance and accountability.
Doug Benoit, CEO FacialDxbelieves that trust begins with clarity. FacialDx is an AI-powered health intelligence company that uses facial analysis technology to identify visual biomarkers associated with health indicators and provide structured observations to support awareness. Benoit explains that users increasingly want to understand how results are achieved rather than just getting more results.
Doug Benoit, CEO of FacialDx
“People want to get the information behind the result,Benoit says.Trust is enhanced when organizations are prepared to show the methodology, data, and rationale that support what the technology delivers.“
This expectation reflects a broader shift in healthcare and technology. Organizations face increasing pressure from regulators, providers, employers and consumers to demonstrate how AI systems work, how data is managed and how human judgment remains involved. “Transparency is no longer seen as an optional extra,” notes Benoit. “For many stakeholders, it is becoming a prerequisite for adoption.“
Privacy is an equally important issue. Healthcare data remains among the most sensitive categories of personal data, Benoit explains, placing significant responsibility on organizations developing AI-powered solutions. Research shows that AI systems that manage sensitive health data raises significant concerns about privacy, data protection and the risk of data breaches, while also highlighting the importance of ensuring that AI supports, rather than overrides, the judgment of healthcare professionals. Benoit believes these considerations reinforce strong governance, security safeguards and clearly defined requirements. human control As AI becomes more integrated into health-related environments.
Benoit notes that the conversation around AI has evolved significantly over the past few years. Many organizations, he says, have moved beyond asking whether to use AI and are now focused on understanding how it can be implemented responsibly within existing workflows.
“The concern we hear most often isn’t whether artificial intelligence will exist,Benoit explains.Organizations want to know how it integrates with what they already do, how information is protected, and whether the technology supports the people responsible for making decisions.“
Human control remains central to this discussion. He explains that while AI can help identify patterns, organize information and improve efficiency, health care decisions often involve context, judgment and interpersonal judgments that go beyond data analysis alone.
Benoit believes that artificial intelligence should be seen as a support tool, not an autonomous body. “Technology can help deploy information faster and more consistently.” he says. “But people still need people. Human oversight provides accountability, interpretation, and the ability to apply professional judgment in a way that technology alone cannot.“
This distinction is becoming increasingly important as organizations define governance frameworks around AI implementation. “Successful implementation often depends on clearly defining what the system is designed to do, what it is not designed to do, and how the results should be interpreted within existing professional processes.” says Benoit.
for FacialDxthis philosophy shapes the company’s position in the healthcare ecosystem. Benoit emphasizes that the platform is designed to provide health intelligence and surveillance insights rather than diagnostic results. Maintaining clearly defined boundaries, he said, promotes responsible adoption while strengthening the role of health professionals in evaluating information and determining appropriate next steps.
It also mentions governance and controlled access as important components of trust. “The goal is to make information accessible, understandable and secure,Benoit says.People need to know who can access their data, how it is managed and what safeguards are in place around it.“
As AI continues to proliferate in healthcare, enterprise health, and telehealth environments, trust may ultimately become the factor that separates short-term experiences from long-term adoption. Innovation remains essential, but continued success will likely depend on whether organizations can keep pace with technological advancements. accountabilitytransparency, privacy protection and human control.
Benoit believes that the future of AI health intelligence will be shaped by this balance. “Organizations that gain trust will be those that maintain transparency, focus on their goals, and use artificial intelligence to support better decisions.” he says. “When innovation and accountability move forward together, people gain confidence in technology and how it is used.“
