{"id":36420,"date":"2025-07-07T08:10:06","date_gmt":"2025-07-07T08:10:06","guid":{"rendered":""},"modified":"-0001-11-30T00:00:00","modified_gmt":"-0001-11-30T00:00:00","slug":"understanding-the-ethical-considerations-surrounding-the-use-of-generative-ai-in-healthcare-and-its-impact-on-patient-privacy-1788377","status":"publish","type":"post","link":"https:\/\/www.simbo.ai\/blog\/understanding-the-ethical-considerations-surrounding-the-use-of-generative-ai-in-healthcare-and-its-impact-on-patient-privacy-1788377\/","title":{"rendered":"Understanding the Ethical Considerations Surrounding the Use of Generative AI in Healthcare and Its Impact on Patient Privacy"},"content":{"rendered":"

Generative AI uses computer programs that can write or speak like humans based on prompts. These AI models, also called large language models (LLMs), are made to understand and create clinical notes, patient talks, and other healthcare writings. For example, the Clinical Natural Language Processing and Artificial Intelligence Innovation Laboratory (PittNAIL) at the University of Pittsburgh develops such models.<\/p>\n

Yanshan Wang, PhD, works on using these AI and natural language processing (NLP) tools to find useful information in electronic health records (EHRs). His projects help improve care for diseases like Alzheimer\u2019s, mental health issues, and cancer by pulling detailed facts from medical notes.<\/p>\n

The progress at PittNAIL shows how generative AI can make healthcare paperwork easier and improve communication with patients. A company called Simbo AI uses AI to answer phones and schedule appointments automatically, helping medical offices run more smoothly without using many human workers.<\/p>\n

While AI tools bring clear help, they also cause hard problems about ethics and privacy that medical workers and IT staff in the U.S. must think about.<\/p>\n

Ethical Challenges Associated with Generative AI in Healthcare<\/h2>\n

AI in healthcare must follow the same ethical rules as all medical work. These include respecting patient choices, doing good, avoiding harm, and being fair. Using generative AI in clinics needs careful thought about these rules.<\/p>\n

Privacy and Data Protection<\/h2>\n

Patient privacy is very important in healthcare and is protected by laws like HIPAA. Generative AI, however, needs lots of data, including sensitive patient details from health records. If this data is accessed or used without permission, it can harm patient privacy.<\/p>\n

Researchers Dariush D. Farhud and Shaghayegh Zokaei point out that AI can have problems like data errors, programming mistakes, and security leaks. Laws such as the EU\u2019s GDPR and the U.S. Genetic Information Nondiscrimination Act (GINA) help protect data but do not cover all AI risks.<\/p>\n

For instance, AI might collect or share more patient data than expected. Without strict access controls and clear patient approval, patients might not know or control how their data is used by AI.<\/p>\n

<\/p>\n

\n
\u2713<\/div>\n
\n

HIPAA-Compliant Voice AI Agents<\/h4>\n

SimboConnect AI Phone Agent encrypts every call end-to-end – zero compliance worries.<\/p>\n

Let\u2019s Chat <\/a>\n <\/div>\n<\/div>\n

<\/p>\n

Informed Consent<\/h2>\n

Patients must agree to share their information or get treatment. When AI tools like Simbo AI\u2019s answering services talk to patients, medical staff must make sure patients know they are talking to AI and understand how their information will be used.<\/p>\n

Doctors and clinics should explain the risks of AI, like mistakes or privacy issues. Patients should be able to say no to AI services but still get human care. The American Medical Association (AMA) says clear talk and honesty help keep trust between patients and providers.<\/p>\n

Bias and Fairness in AI Systems<\/h2>\n

AI can sometimes be unfair. Bias may cause wrong suggestions, wrong diagnoses, or unequal care. This bias can come from different places:<\/p>\n