The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review

Large language models (LLMs) have emerged as a transformative force in artificial intelligence (AI), generating significant interest across various sectors. The 2022 launch of OpenAI’s ChatGPT demonstrated their groundbreaking capabilities, revealing the current state of development to a wide audience. With the introduction of ChatGPT, Large Language Models (LLMs) have received enormous attention in healthcare.

Applications and Potential Benefits

A particularly rapid adoption of LLMs is seen in medicine and healthcare, encompassing clinical, educational and research applications. Present-day LLMs, such as ChatGPT, are considered to have a promising accuracy in clinical decision-making, diagnosis, symptom-assessment, and triage-advice. In patient-communication, it has been posited that LLMs can also generate empathetic responses. Advantages of using LLMs are attributed to their capacity in data analysis, information provisioning, support in decision-making or mitigating information loss and enhancing information accessibility.

Specifically, four general fields of applications emerged showcasing a dynamic exploration phase:

• Clinical applications
• Educational applications
• Research applications
• Patient care and communication

Ethical Concerns and Risks

Despite potential benefits, researchers have underscored various ethical implications. The adoption of LLMs is entwined with ethical and social concerns. The healthcare and medical fields, being particularly sensitive and heavily regulated, is notably susceptible to ethical risks. Our study identifies recurrent ethical concerns connected to fairness, bias, non-maleficence, transparency, and privacy. A distinctive concern is the tendency to produce harmful or convincing but inaccurate content.

Key Ethical Landscape Findings

• Fairness and Bias: Potential risks across various application domains.
• Non-maleficence: Risks of producing harmful or inaccurate content.
• Privacy: Data sensitivity in the medical field.
• Transparency: Necessity for clear documentation and performance certainty.

The Need for Ethical Guidance

Calls for ethical guidance and human oversight are recurrent. We suggest that the ethical guidance debate should be reframed to focus on defining what constitutes acceptable human oversight across the spectrum of applications. This involves considering the diversity of settings, varying potentials for harm, and different acceptable thresholds for performance and certainty in healthcare. Additionally, critical inquiry is needed to evaluate the necessity and justification of LLMs’ current experimental use.

Systematic Review Methodology

This work maps the ethical landscape surrounding the current deployment of LLMs in medicine and healthcare through a systematic review. Electronic databases and preprint servers were queried using a comprehensive search strategy which generated 796 records. Studies were screened and extracted following a modified rapid review approach. Methodological quality was assessed using a hybrid approach. For 53 records, a meta-aggregative synthesis was performed.