A survey done by the American Medical Association (AMA) asked 1,081 doctors about AI in healthcare. Almost two-thirds of these doctors see benefits in AI. They said AI helps improve diagnostics (72%), makes clinical work more efficient (69%), and improves outcomes (61%). But only 38% actually use AI in their work. This shows a gap between what doctors believe and what they do. It points to problems with trust, honesty, and understanding.
Doctors worry most about patient privacy and how AI might affect the patient-doctor relationship. About 41% are concerned about data privacy. Another 39% think AI could harm trust and communication between patients and doctors. This highlights how important human connection and personal care are to patients.
Healthcare workers also feel unsure because AI systems are not clear. More than 60% say they find it hard to use AI because they don’t understand how AI makes decisions or how its performance is tracked. Without clear information, they hesitate to fully rely on AI.
Patients trust AI even less than healthcare workers. A study by Philips shows a “trust gap” about AI’s role in healthcare. While 63% of healthcare workers are hopeful that AI can improve outcomes, only 48% of patients agree. This overconfidence varies by age. Younger patients under 45 are more positive (66%) compared to older adults (33%). For healthcare leaders, this means different communication approaches are needed for different age groups.
Patients mainly worry about safety, losing the human touch, who is responsible, and transparency. They want to know that doctors and nurses are still in charge, not AI working on its own. Trust goes down when patients think AI replaces human judgment. But trust goes up when they know AI helps doctors make decisions, not replace them.
Most importantly, 79% of patients trust their doctors and nurses to tell them about AI. This makes healthcare workers very important as explainers. Hospitals and clinics must train staff to explain AI clearly and kindly so patients can see how AI helps.
It is important to be open about how AI works to build trust with both healthcare workers and patients. Explainable AI (XAI) reveals how AI makes choices. When doctors understand AI decisions, they trust it more. Also, explainability helps reduce worries about hidden bias, mistakes, or unfairness.
Ethics also matters a lot. Bias in AI can cause unfair care for some groups and make health differences worse. Good AI needs data from different groups and constant checks to find and fix bias. Healthcare workers want AI to be fair, equal, and responsible.
Another key issue is cybersecurity. In 2024, a data breach called WotNot showed that AI systems can be vulnerable. This put patient data at risk and raised concerns about how safe AI tools are in healthcare. Medical offices keep very private patient information. Any breach can cause serious harm and loss of trust.
Because of these reasons, policymakers must set up ethical rules, safety standards, and transparency requirements. These rules help healthcare workers feel that AI tools are safe, trustworthy, and follow ethical laws. Without these rules, many health centers may not fully use AI.
The rules for AI in healthcare are unclear in the United States. There are some guidelines, like the White House’s AI Bill of Rights and the NIST AI Risk Management Framework (AI RMF 1.0), but no single set of rules just for AI in healthcare.
Most healthcare workers—about 78% in the AMA survey—say clear and steady rules are needed. Good rules would cover questions about who is responsible, safety, transparency, and ethical use. Right now, clinicians often take the blame for AI mistakes even when they don’t fully understand how the AI works. This is unfair because many errors come from how AI systems are made or used.
Policymakers must find a balance between safety and innovation. They should make rules that help AI grow but also protect patients and follow ethical standards. For example, a British guideline called BS30440 checks that AI products meet safety and fairness rules. The U.S. could make similar standards.
Also, HITRUST created the HITRUST AI Assurance Program. It combines NIST and ISO standards to guide safe AI use. Big cloud companies like Microsoft, AWS, and Google use this program. This shows how combined industry standards can build trust and security.
Clear rules are not enough. Healthcare workers also need to be ready and know about AI to trust and use it. Research shows many providers don’t know enough about AI, which causes fear or doubt. Training programs for doctors and nurses can teach them what AI can and cannot do, and its ethical issues.
Healthcare groups and policymakers should invest in teaching the workforce. This can include hands-on AI use, talks about ethics, data skills, and privacy and security lessons. Training should help create a mindset that AI supports decisions but does not replace doctors. This can reduce resistance to AI.
The PULsE-AI program in England shows some barriers. Even with good AI for heart screening, it was hard to use because it didn’t work well with the existing computer systems and workflows. Also, there was little training. Solving these kinds of problems can help AI grow in U.S. healthcare too.
AI is also useful for automating office tasks in medical practices. Companies like Simbo AI offer tools to automate phone calls, appointment bookings, insurance checks, and other tasks. This helps staff by reducing their workload, cutting human errors, and speeding up patient service.
Doctors in the AMA survey were positive about AI’s ability to automate writing notes (54%) and handling insurance authorizations (48%). Medical offices have heavy paperwork. AI can save staff a lot of time so they can focus on patients.
To get the most from automation, AI tools must work smoothly with existing electronic health record (EHR) and office management systems. Problems happen when these systems do not connect well. Policymakers can push for standard data formats and rules to make joining these systems easier.
It is also important for automation tools to be clear and explainable. Office managers want to know how the system decides which calls to handle first or how it manages billing. This helps avoid mistakes that hurt patients or disrupt work. When the AI’s “black box” is open, organizations can watch how well it works and keep it accountable.
Progress in healthcare AI depends on working together. Healthcare workers, AI makers, regulators, policymakers, and patients all need to connect. Policymakers should promote open talks and create systems that include everyone’s views, especially cautious patients.
Public trust requires clear communication, being open, behaving ethically, and knowing humans are still in charge. Rules need to protect people but also allow new ideas. Healthcare groups should get support with funding, education, and technology to use AI safely and well.
Trust is very important. Without it, AI use will slow down and health care will miss chances to improve patient care, efficiency, and quality. Gaining trust requires ongoing work in openness, learning, ethics, and clear rules. These are goals the U.S. healthcare system can reach with good policymaker teamwork.
By working on these areas, policymakers can help healthcare AI grow stronger. This will support confident healthcare workers and reassure patients. Medical managers, practice owners, and IT staff will then be able to use AI tools that improve healthcare across the United States.
Physicians have guarded enthusiasm for AI in healthcare, with nearly two-thirds seeing advantages, although only 38% were actively using it at the time of the survey.
Physicians are particularly concerned about AI’s impact on the patient-physician relationship and patient privacy, with 39% worried about relationship impacts and 41% about privacy.
The AMA emphasizes that AI must be ethical, equitable, responsible, and transparent, ensuring human oversight in clinical decision-making.
Physicians believe AI can enhance diagnostic ability (72%), work efficiency (69%), and clinical outcomes (61%).
Promising AI functionalities include documentation automation (54%), insurance prior authorization (48%), and creating care plans (43%).
Physicians want clear information on AI decision-making, efficacy demonstrated in similar practices, and ongoing performance monitoring.
Policymakers should ensure regulatory clarity, limit liability for AI performance, and promote collaboration between regulators and AI developers.
The AMA survey showed that 78% of physicians seek clear explanations of AI decisions, demonstrated usefulness, and performance monitoring information.
The AMA advocates for transparency in automated systems used by insurers, requiring disclosure of their operation and fairness.
Developers must conduct post-market surveillance to ensure continued safety and equity, making relevant information available to users.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
Schedule AI Agents Demo Now© Since 2019, Simbo AI.
