Trustworthy AI means artificial intelligence systems that are made and used following strict rules for safety, reliability, openness, fairness, and responsibility. Healthcare providers in the U.S. face big challenges when using AI because they worry about patient safety, data security, bias, and following the law. For example, research in eye care shows that AI can perform as well as or better than human experts in clinical work, but using AI in real practice is often slowed down because people do not fully trust it or understand how it works.
Cristina González-Gonzalo and others say that making trustworthy AI is not the job of just one group. It needs teamwork from AI developers, healthcare workers, regulators, leaders, patients, and insurance companies. Working together ensures AI systems are correct, safe, dependable, and responsible, from the start to after they are used.
The U.S. healthcare system has many rules and different kinds of patients. This teamwork helps make sure AI products follow local laws, like HIPAA privacy rules, and meet cultural needs that affect patient acceptance.
Stakeholder engagement means including everyone affected by or involved in making and using AI. This involves healthcare leaders, doctors, patients, experts in technology, ethics, and policy makers. A study published by the MICCAI Society shows that including many different people early and throughout AI projects is very important. This helps lower risks of bias, exclusion, and lack of openness that can stop AI from working well and being accepted in real life.
For administrators and IT managers in U.S. medical offices, this means working with clinical staff and the technical and admin teams who understand daily work and patient care well. Patients also play a key role because their views give ideas about ethics, privacy, security, and how AI might affect their feelings.
Including experts in ethics and law makes sure AI tools follow U.S. laws and values. Policy makers help match projects with health policies and payment rules. Together, these groups help create AI that works well and can be trusted.
If there is no teamwork, AI development might not match what healthcare places need or can safely use. For instance, Frank Verbraak and his team showed that not involving healthcare workers and regulators early can slow down using AI, even if the technology is very good.
In medical practice, trust depends on how correct, steady, open, and clear the AI system is. For administrators and IT managers in U.S. clinics, knowing this is important because work plans, staff training, and patient education all depend on users trusting the AI.
Some questions to answer together include:
Working together on these questions helps add AI tools that help without causing new problems.
A study by Beatriz Alves and the AI-PROGNOSIS team showed how stakeholder input guides making trustworthy AI. They focused on AI tools for Parkinson’s disease but their lessons apply to many healthcare areas.
The study workshops involved 24 people, about half living with Parkinson’s, and the rest were doctors, AI developers, bioethics experts, and group leaders. Five main ideas came out:
Medical leaders and IT workers can use these lessons to choose AI tools that not only work well but are trustworthy from many views.
Another important point is setting clear expectations about what AI can and cannot do. Marjorie Kinney and others made a way to match what users expect with what AI actually does in clinics. Their study interviewed healthcare workers who often fear that overselling AI causes people to lose trust when it fails.
In practice, this means leaders must talk openly about what AI can do, its limits, and the need for human oversight. Clear expectations help avoid disappointment and make sure people see AI as a tool that assists, not replaces, humans.
For medical practice leaders and IT managers, AI affects more than diagnosis and clinical decisions. A growing use is in front-office work like answering phones and talking with patients. Simbo AI is one example of a company that makes AI for phone automation in healthcare offices.
Using AI systems like Simbo AI’s phone answering service can free staff from routine calls, letting them spend more time on clinical and patient care. AI can handle tasks like scheduling appointments, answering common questions, and routing calls, which cuts wait times and helps patient satisfaction.
To add these AI tools well, clinics must consider:
By automating phone and admin chores, U.S. medical offices can become more efficient while keeping good care and patient involvement. This also fits with the team approach by including admin staff in AI design and use.
The U.S. healthcare system has many rules to keep patients safe but this can slow down new ideas. Agencies like the Food and Drug Administration (FDA) watch over AI tools that count as medical devices. These tools must be tested well and watched both before and after they are allowed to be used.
Research by Cristina González-Gonzalo and others shows that following rules is key to trustworthy AI. Healthcare leaders must know that following laws is ongoing, not just a one-time step. It includes watching AI after it starts being used to catch any unexpected problems fast.
This need for rules means AI developers, healthcare leaders, doctors, and regulators must work together. This shared job keeps safety and ethics high while bringing new solutions to providers in the U.S.
Making and using AI in healthcare needs help from tech teams, doctors, ethicists, and leaders. This makes sure AI is both technically good and fits how hospitals and clinics work.
Karim Lekadir and others say that ongoing talks, learning, and open involvement are important. This cooperation makes sure AI respects fairness, responsibility, and patient rights, while fitting into clinical and work routines.
Medical leaders have a special role to encourage teamwork by supporting regular communication among:
Working like this helps create a healthcare environment where AI acts like a trusted helper, not a confusing tool.
This article shows how involving many different groups affects making, accepting, and safely using AI in U.S. healthcare. Medical leaders, owners, and IT managers should know that trustworthy AI needs openness, inclusion, and teamwork from start to ongoing use.
Working with everyone involved—from patients and doctors to developers and regulators—helps make AI tools that meet real needs and follow U.S. laws and healthcare ways. When combined with AI that automates office work like Simbo AI’s phone answering, these changes can help make healthcare offices run better, provide good patient care, and use AI ethically.
Good AI use in U.S. healthcare depends not only on how well the technology works but also on strong relationships and communication among all people who bring these tools into practice.
The article focuses on the need for a framework that manages expectations regarding trust and acceptance of artificial intelligence systems, especially in healthcare.
Expectation management is essential to align stakeholder anticipations, which helps in harnessing the benefits of AI while mitigating associated risks.
The framework aims to capture end-user expectations for trustworthy AI systems, facilitating discussions about user needs and system attributes.
The study engaged fourteen diverse end users from healthcare and education sectors, including physicians and teachers.
The framework was validated through semi-structured interviews that included questions based on its constructs and principles.
A qualitative analysis revealed pivotal themes and differing perspectives among interviewee groups regarding AI trust and implementation challenges.
The framework is significant as it guides discussions on user expectations and highlights potential challenges in effective AI system implementation.
The framework underscores the importance of explainability in AI systems, essential for building trust among users.
The interviews primarily focused on perspectives from healthcare and education, showcasing the framework’s relevance across sectors.
The challenges include aligning user expectations with system capabilities, which could undermine the efficacy of AI technologies in practice.
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