Addressing the ethical implications of transparency and disclosure when deploying autonomous AI agents in healthcare settings to ensure patient informed consent

Autonomous AI agents are computer programs made to do tasks with little or no human help. In healthcare, they can be virtual assistants that schedule appointments, answer patient questions, or help with diagnoses. These tools can make work easier, but there are concerns about how clearly they tell patients who they are and what they can do.

One big ethical issue is deception. This means the AI agents do not clearly say they are not human. Some AI systems, like Google Duplex from 2018, use speech that sounds like people and have conversations that can trick patients into thinking they are talking to a real person. This can mislead patients and make it hard for them to give true informed consent. If patients think they are talking to a human, they might share personal information or trust the system more than they should.

Research shows that about 1% of young adults even called chatbots their friends or romantic partners, which shows how real these interactions can feel. This kind of unintended deception raises questions about trust, personal freedom, and respect for people.

AI agents can also use manipulation, which means taking advantage of how people think or feel to change what they do. This is not seen as ethical, even if the AI seems to be helping. For example, an AI scheduling tool that pushes patients to choose earlier appointments by making it seem urgent might limit patients’ freedom to decide. This kind of hidden pressure can harm informed consent and respect for patients.

Legal rules are changing because of these problems. Courts in the U.S. and Europe now say AI systems are not just tools without responsibility. Companies must take responsibility for harms caused by AI. The 2024 EU AI Act, for example, has rules for when AI causes harm linked to deception or manipulation. This sets new global standards for how AI should be used carefully.

In the U.S., laws are also changing. Courts refuse to treat AI as separate legal beings and hold companies accountable for AI harms. A 2023 Supreme Court decision removed some protections for companies, pushing them to be more responsible for AI accuracy and ethics.

Importance of Transparency to Patient Informed Consent

Transparency is very important in healthcare ethics. It means being clear about how AI is used with patients. For patients to give real informed consent, they need to know:

• That AI is part of their care or communication.
• What the AI can and cannot do.
• Possible risks, like mistakes or data privacy issues.
• Their right to accept or refuse AI services.

The SHIFT framework stands for Sustainability, Human centeredness, Inclusiveness, Fairness, and Transparency. It helps guide how AI should be used responsibly in healthcare. Transparency means clearly telling patients when AI is involved in anything that affects their health or personal data. Many current AI systems do not openly say this, which can break informed consent rules.

Katy Ruckle, a healthcare AI ethics expert, says transparency means using simple, clear language that patients can understand no matter their technical skill. Using real-life examples can help explain how AI affects care. It is also important to let patients ask questions and say no to AI without being treated badly.

Transparency is not just at the start. Patients should get updates about how accurate or reliable AI is during their care. Keeping this communication open helps build trust and lets patients stay in control of their care.

Addressing Bias, Privacy, and Accountability Alongside Transparency

Transparency alone is not enough. AI also can bring biases if it is trained on unfair or incomplete information. In healthcare, biased AI can treat some patient groups unfairly and make health differences worse. Checking AI regularly, using different data sources, and sharing how it works openly help reduce these risks.

Patient privacy and data security are very important too. AI agents need lots of health data to work well. Protecting this data with encryption, anonymizing personal information, and limiting who can see it helps stop unauthorized access or misuse. Healthcare providers must follow HIPAA and other rules when using AI.

Another issue is automation bias, where staff rely too much on AI without enough careful thinking. Some studies show fewer diagnostic tests are used after AI is introduced, which might mean people trust AI too much. Staff need ongoing training to question AI results, get second opinions, and balance AI advice with human judgment.

AI and Workflow Automation: Enhancing Ethical Implementation

Healthcare leaders and IT managers can use autonomous AI agents for front-office tasks like scheduling, patient reminders, and answering calls to improve efficiency and patient service. But these tools must be designed with ethics and patient consent in mind.

AI phone services, like those from Simbo AI, can handle many calls, reduce wait times, and keep patients engaged. But patients must know they are talking to AI, not a human. Simple statements at the start of calls help make this clear.

AI in workflows should also:

• Respect patient choices and let them easily switch to a human if they want.
• Avoid language that pressures or manipulates patients.
• Explain clearly what AI is doing during reminders or follow-ups.
• Keep patient data safe during calls.

Using AI responsibly means staff must understand what AI can and cannot do. Training should focus on working with AI as a helper, not a replacement for doctors’ or nurses’ judgment and care.

As AI gets more independent, healthcare groups need rules to watch AI performance, find ethical problems, and update policies. Getting feedback from patients and staff helps improve AI while keeping ethics strong.

Implications for Medical Practice Administrators, Owners, and IT Managers in the United States

Using autonomous AI agents in healthcare brings both chances and duties. Medical practice leaders in the U.S. need to handle ethical issues about transparency and consent while laws and social expectations change.

Healthcare providers should:

• Create clear policies so patients know when AI handles their requests or data.
• Train staff on AI ethics, focusing on transparency, patient rights, and data care.
• Set up audits and oversight to watch AI and stop deception, manipulation, and bias.
• Protect patient privacy according to HIPAA with best data security practices.
• Communicate so patients easily understand AI use, risks, and options.
• Respect patients’ right to refuse AI services without lowering their care quality.

AI is growing in healthcare office work. Balancing better efficiency with ethical care is important. Groups like Salesforce show how companies are thinking about ethics in their AI plans. Washington state’s AI Community of Practice works on state-level rules that could guide the whole country.

Legal Considerations on Liability and Ethical Responsibility

Healthcare leaders should know that laws are changing to hold companies more responsible for AI actions. Courts no longer accept the idea that AI is just a tool with no liability. For example, a case with Air Canada showed that companies cannot avoid responsibility for AI mistakes.

This means healthcare providers must make contracts that clearly state who is responsible for AI problems. Vendors should be required to keep AI transparent, prevent manipulation, and regularly check AI performance.

Using ethical AI design helps avoid legal trouble and meets new standards. The SHIFT framework’s focus on fairness and sustainability can help organizations build rules that include liability.

Summary

Autonomous AI agents are becoming more common in healthcare front-office tasks. They can make care easier to access and improve patient satisfaction. But being open and honest about AI’s role is needed to respect patients and get real informed consent. Medical practice leaders, owners, and IT staff play an important role in making policies, training staff, and setting safeguards. These steps help stop deception and manipulation while protecting privacy and reducing bias. With good planning and careful use, healthcare groups can manage these new tools responsibly.