Ethical Considerations of Using ChatGPT in Health Care

Abstract

ChatGPT has promising applications in health care, but potential ethical issues need to be addressed proactively to prevent harm. ChatGPT presents potential ethical challenges from legal, humanistic, algorithmic, and informational perspectives. Legal ethics concerns arise from the unclear allocation of responsibility when patient harm occurs and from potential breaches of patient privacy due to data collection. Clear rules and legal boundaries are needed to properly allocate liability and protect users. Humanistic ethics concerns arise from the potential disruption of the physician-patient relationship, humanistic care, and issues of integrity. Overreliance on artificial intelligence (AI) can undermine compassion and erode trust. Transparency and disclosure of AI-generated content are critical to maintaining integrity. Algorithmic ethics raise concerns about algorithmic bias, responsibility, transparency and explainability, as well as validation and evaluation. Information ethics include data bias, validity, and effectiveness. Biased training data can lead to biased output, and overreliance on ChatGPT can reduce patient adherence and encourage self-diagnosis. Ensuring the accuracy, reliability, and validity of ChatGPT-generated content requires rigorous validation and ongoing updates based on clinical practice. To navigate the evolving ethical landscape of AI, AI in health care must adhere to the strictest ethical standards. Through comprehensive ethical guidelines, health care professionals can ensure the responsible use of ChatGPT, promote accurate and reliable information exchange, protect patient privacy, and empower patients to make informed decisions about their health care.

Utility of ChatGPT for Automated Creation of Patient Education Handouts: An Application in Neuro-Ophthalmology

“…The use of ChatGPT to produce patient education handouts confers a set of ethical concerns, related to legal, humanistic, and algorithmic issues. 17 Given the recent advent of AI, it remains not entirely clear how the burden of legal responsibility should be partitioned between physician and OpenAI, especially for cases of patient harm and/ or privacy breaches. 17 Ultimately, clearer rules on the use of AI and related responsibilities in health care are yet to be instated.…”

“…17 Given the recent advent of AI, it remains not entirely clear how the burden of legal responsibility should be partitioned between physician and OpenAI, especially for cases of patient harm and/ or privacy breaches. 17 Ultimately, clearer rules on the use of AI and related responsibilities in health care are yet to be instated. 17 One safe approach involves carefully reviewing the entirety of the initial draft, with an understanding of assuming full responsibility for any harms, necessarily in exchange for the efficiency of the model.…”

“…17 Ultimately, clearer rules on the use of AI and related responsibilities in health care are yet to be instated. 17 One safe approach involves carefully reviewing the entirety of the initial draft, with an understanding of assuming full responsibility for any harms, necessarily in exchange for the efficiency of the model. Furthermore, it is likely the best practice to implement manual and offline personalization of patient educational handouts after online draft generation, to avoid entering patient-sensitive information into the model.…”

“…A humanistic ethical issue is that of integrity. 17 Indeed, the model would assemble an education handout based on its training set, which includes other educational materials with potential usage restrictions. 17 Given this, recommendations involve including a disclaimer of partial AI-generation in a patient handout, revising the draft to optimize readability and originality, and distributing the handout without means of further personal gain besides workflow efficiency.…”

What Does ChatGPT Mean for Qualitative Health Research?

“…There is also dialogue on the use of AI in publishing covering topics such as editorial decision-making (COPE Council, 2021). In comparison, in the broader literature, there is a burgeoning of publications on AI related to authorship, ethics, plagiarism, bias, accuracy, performance, and scientific integrity (see, for example, Ashraf & Ashfaq, 2023;Baker et al, 2023;Cohen et al, 2023;Conroy et al, 2023;Dergaa et al, 2023;Dien, 2023;Doshi et al, 2023;Doyal et al, 2023;Farina & Lavazza, 2023;Garcia, 2023;Jeyaraman et al, 2023;Leung et al, 2023;Meyer et al, 2023;Ruffle et al, 2023;Takeda, 2023;Wang et al, 2023). However, there is a paucity of academic research on ChatGPT and qualitative research (Tabone & de Winter, 2023).…”