The Ethical Implications of AI in Medicine: Should AI Systems Be Treated as Legal Entities with Accountability?

AI systems now handle a large part of healthcare interactions. By 2023, AI was expected to deal with about 30% of customer service contacts worldwide. Healthcare uses AI chatbots and virtual assistants for patient questions, appointment booking, and first symptom checks. But AI can make mistakes because it relies on data and programming.

These mistakes happen when data is missing, biased, or wrong. For example, AI might misunderstand symptoms or suggest the wrong next steps. This can be serious because medical decisions affect patient safety.

Figuring out who is responsible for AI mistakes is hard. Usually, responsibility might go to users, programmers, owners, or healthcare institutions using AI. But currently in U.S. law, AI is seen as property without rights or duties. This leaves a gap when trying to hold someone accountable for AI errors.

Robayet Syed, a PhD student in business law, explains this is a tricky problem. Legal experts often must decide on responsibility case by case because linking specific AI mistakes to people is difficult. Syed also asks if AI itself should be responsible since it’s hard to prove a person caused every error.

Understanding Legal Personhood as Applied to AI

In the U.S., legal personhood means an entity has rights and responsibilities under the law. People and corporations can be held responsible because they have this status. But AI systems are considered property. They do not have awareness or intentions and so cannot be legally responsible.

Some legal experts and places have talked about giving AI limited legal personhood in some situations. This could mean AI could be sued or held liable for harm it causes. This idea is similar to old cases where courts viewed corporations as separate legal entities from their owners.

However, this raises hard questions. AI does not have awareness or moral sense. It works only by following programming and data given by humans. Is it fair to blame something that does not understand or intend actions?

The legal idea of vicarious liability is related. It holds employers responsible for their employees’ actions during work. Some suggest AI could be treated like a company agent. If AI causes harm while controlled by a healthcare provider or developer, those people could be responsible.

Right now, in the U.S., humans managing AI—like hospital leaders, doctors, or developers—are usually held responsible.

Ethical Concerns: Transparency, Bias, and Human Rights in AI Healthcare Systems

Besides who is legally responsible, AI use in healthcare raises other ethical concerns that affect patients and providers.

• Algorithmic Transparency: A big issue is not knowing how AI reaches decisions. In medicine, trust and clear explanations are very important. AI can be like a “black box,” making it hard for patients or doctors to question its advice. This can be unfair and cause problems with informed consent.
• Bias and Discrimination: AI learns from past data, which may have biases. If minority groups are underrepresented in data, AI might make unfair decisions. Researchers have warned that biased AI can increase health disparities, especially for vulnerable groups.
• Data Privacy and Cybersecurity: AI in healthcare needs access to a lot of personal health data. This raises the risk of data leaks that can harm patient privacy. Hackers, including companies like drug makers or insurers, might try to steal information. Healthcare managers must protect data well.
• Misinformation and Clinical Judgment: AI can produce medical advice that looks correct but is wrong. This can harm trust between doctors and patients. There is also a worry that doctors might rely too much on AI and lose critical thinking skills.

Researcher Nikolaos Siafakas suggests adding ethical rules to AI, like the Hippocratic Oath that doctors take. This could help make AI use more responsible and safe for patients.

AI and Workflow Integration in Medical Practices: Practical and Ethical Dimensions

Healthcare administrators and IT managers in the U.S. often use AI to improve both medical care and administrative work. AI can help with things like answering phones, scheduling, and managing patient communication. This can save money and make work easier.

For example, Simbo AI uses AI to automate phone calls and answer common patient questions fast. This lets human staff focus on harder tasks and improves workflow. But AI automation has some risks too:

• Error Handling: Automated systems might misunderstand questions or miss emergencies, which can be dangerous.
• Patient Trust: Patients want clear, reliable communication. If automation causes confusion or delays, trust in healthcare providers can drop.
• Data Security: Phone systems that handle patient data must follow privacy laws like HIPAA to keep information safe.
• Liability: When AI automation makes mistakes, like giving wrong appointment times, it’s hard to say who is responsible—the AI developer, the healthcare institution, or the staff.

Medical administrators and IT staff need to understand these issues. AI can help with workflow but must be carefully managed, with staff training and clear legal rules to avoid problems.

Legal Frameworks and Ongoing Challenges in the United States

As AI grows in U.S. healthcare, the laws about AI responsibility are still not clear. Current rules do not fully say who is responsible if AI causes harm. Some important points are:

• Case-by-Case Liability: Courts usually look at AI cases one by one because it is hard to prove fault directly.
• No AI Legal Personhood: AI cannot be sued by itself now. Lawsuits target companies, programmers, or healthcare providers.
• Vicarious Liability: Healthcare groups that use AI might be held responsible for harm caused by AI under their control.
• Regulatory Efforts: The European Union has an AI law focusing on safety and accountability, but the U.S. has no complete AI law for healthcare yet.
• Ethical Codes for Developers: Experts propose codes of ethics for AI developers, like doctors have, to improve responsibility and safety.

For U.S. healthcare leaders and lawyers, the uncertainty means they should be careful. Contracts for AI tools should clearly say who is responsible. Staff need training on AI limits and strict oversight is necessary.

It is important to see AI as a tool controlled by humans, not an independent agent. This helps make responsibility clearer.

The Role of AI in Clinical and Administrative Decision-Making: Balancing Benefits and Risks

Although there are ethical and legal challenges, AI also helps healthcare in many ways. AI programs can look at large amounts of data to find patterns, predict risks, and help with diagnosis faster than humans alone. Administrators can use AI to improve patient communication and manage resources better, saving money and improving service.

But there is still a big gap between what AI could do and using it safely and fairly. Healthcare workers should balance adopting AI with ways to reduce risks like these:

• Making sure data used to teach AI is complete and fair to avoid bias.
• Setting transparency rules so decisions by AI can be explained and questioned.
• Using strong cybersecurity to protect patient data.
• Keeping human doctors as the main decision makers to avoid overreliance on AI.
• Adding ethical rules for creating and using AI, including ways to hold people accountable.

AI Accountability and Legal Personhood: What It Means for Healthcare Practice Administrators

If in the future AI systems were given legal personhood, healthcare managers, owners, and IT staff in the U.S. would face new challenges. AI could be sued and held responsible directly. This would change how insurance, compliance, and risk management work. It might require:

• Regular checks of how AI makes decisions.
• Reports from AI companies on ethical compliance.
• New contracts that include AI as a party responsible for actions.
• Changes to how workflows are managed to include AI oversight.

Right now, AI has no legal status on its own. Humans—owners, operators, and programmers—are responsible. Laws may change over time, but current priorities are transparency, risk reduction, and clear legal rules that protect patients and providers.

Final Thoughts on the Use of AI in Medical Settings

AI is a useful tool for healthcare in the U.S. It can help with patient engagement, lessen work for staff, and support medical decisions. But there are many ethical and legal issues with its use. Medical managers should:

• Know the limits of AI responsibility.
• Demand clear information about how AI makes decisions.
• Work on fixing bias and protecting privacy.
• Keep up with changing laws.
• Train staff to understand AI is a tool, not the decision maker.

As AI advances, it is important to use it in ways that are safe and fair. Clear rules and ethics are needed to protect patients, doctors, and healthcare organizations in this AI-driven system.