Addressing the Gaps in Responsible AI Literature: A Call for Clarity and Depth in Governance Frameworks

Responsible AI governance means having clear rules and practices for how AI is made, used, checked, and reviewed to keep it safe, fair, clear, and honest. In healthcare, this is very important. It helps protect patient privacy, reduce unfair treatment, and follow laws.

Healthcare is different because patient information is sensitive, fast and correct communication is needed, and AI can affect patient care. AI tools in office tasks, like automated phone answering, must be carefully designed and managed to avoid mistakes or confusion that could upset patients or affect their care.

In a 2025 article in The Journal of Strategic Information Systems, researchers Emmanouil Papagiannidis, Patrick Mikalef, and Kieran Conboy combined several studies to suggest a framework for responsible AI governance. Their framework has three main parts:

• Structural practices: Rules, roles, and organizations that decide who is responsible for overseeing AI systems.
• Relational practices: Communication and teamwork among healthcare staff, IT workers, patients, and regulators.
• Procedural practices: Steps for designing, using, watching, and checking AI tools.

The researchers point out that while broad ethical rules exist globally, healthcare groups often find it hard to turn these rules into clear actions for daily work.

The Current Gap in Responsible AI Literature for U.S. Medical Practices

Many papers and guides talk about responsible AI ideas like ethics, fairness, and openness. But they often do not explain clearly how to use these ideas in U.S. healthcare.

Medical office leaders and IT managers in the U.S. face scattered and unclear advice. They are often unsure about:

• How to add AI rules into current medical and office work
• Who should be involved in managing AI tools
• How to check AI’s effect on patient care and office work
• How to hold people responsible when AI makes choices or affects work

This confusion comes partly because AI is spreading fast into many office jobs without enough focus on making sure it fits healthcare laws like HIPAA and patient safety rules. Also, many studies do not deal with the full life of AI systems, which need constant watching and changes.

The Importance of Governance Components in AI Deployment

The three parts of AI governance—structural, relational, and procedural—are important to face these problems.

1. Structural practices mean setting clear rules about ethical use, data care, and job duties with AI. Medical offices should have committees or officers to oversee AI tools. These roles must follow U.S. healthcare laws.
2. Relational practices focus on open communication among everyone involved. This includes healthcare workers using AI, patients trusting AI with private information, and IT staff who manage the AI. Good communication helps avoid mistakes or lack of trust.
3. Procedural practices involve steady processes for AI from start to finish. Regular checks can find bias or errors early. This is important because AI often helps with patient calls and office choices.

Regulatory and Ethical Concerns in the U.S. Healthcare Context

In the U.S., healthcare providers must follow many federal and state rules to protect patient data and ensure good care. Laws like HIPAA set data privacy rules, but it is unclear how these rules apply to new AI systems that learn and change over time.

Research stresses the need to match AI governance with laws to avoid problems such as:

• Fair AI decisions that do not discriminate against any group
• Clear communication explaining AI decisions to patients and staff
• Systems that allow humans to fix AI mistakes
• Strong privacy protections following data rules

Without clear frameworks, medical offices in the U.S. risk breaking laws, losing patient trust, or facing problems from poorly managed AI.

AI and Workflow Integration: Enhancing Front-Office Efficiency with Responsible Automation

One main use of AI in healthcare offices is automating phone calls and answering services like those by Simbo AI. These AI tools handle calls, book appointments, give information, and direct questions quickly. When managed well, AI answering services reduce wait times, cut staff work, and improve patient interactions.

To do this right, AI must fit with existing work rules, including:

• Design for patient safety: AI must understand patient requests correctly to avoid delays or frustration.
• Be clear: Patients should know when they are speaking with AI instead of a person.
• Regular training and testing: AI should be updated and tested often to stay accurate and fair.
• Accountability and human control: Staff need ways to step in when AI cannot handle a case properly.

With these rules in place, office managers and IT staff can use AI to make work smoother without hurting care or breaking rules.

Challenges in Operationalizing Responsible AI Governance

Because AI governance advice is scattered, U.S. healthcare offices face real problems:

• Turning Ethics into Actions: Broad ethical ideas can be hard to turn into clear healthcare rules.
• Aligning Stakeholders: Clinics must get doctors, IT workers, regulators, and patients to share AI management duties.
• Ongoing Governance: AI keeps changing, so governance must be flexible. This means constant checks, risk studies, and updates.
• Documenting and Reporting: Keeping good records on AI decisions helps meet rules and catch problems early.

Solving these problems needs healthcare offices to go beyond basic rules. They should build detailed governance plans that fit their specific needs with help from ongoing research.

The Way Forward: Research and Practice Recommendations

The framework by Papagiannidis and others offers a base for better AI governance in healthcare. But it needs more research and practical tests. They suggest:

• Healthcare groups should try and change governance frameworks to fit their size, type, and tech level.
• Researchers should study how the three governance parts affect AI results in different healthcare settings.
• Laws should clearly state AI governance needs based on research.
• Medical leaders and IT staff should join in making governance rules that match real office work.

Working on better governance will help close the gap between broad ideas and hands-on guidance. This supports using AI in healthcare in a careful and honest way.

Healthcare leaders and IT managers, especially those using AI phone systems like Simbo AI’s, can gain from using these governance ideas. They will improve patient calls and office work, while keeping the ethical standards needed for trusted healthcare.

By making governance clearer and deeper, healthcare groups can handle AI challenges with more confidence and responsibility. This will help improve care and protect patient rights.