The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on Large Language Models (LLMs) (2403.14473v1)

Abstract: With the introduction of ChatGPT, LLMs have received enormous attention in healthcare. Despite their potential benefits, researchers have underscored various ethical implications. While individual instances have drawn much attention, the debate lacks a systematic overview of practical applications currently researched and ethical issues connected to them. Against this background, this work aims to map the ethical landscape surrounding the current stage of deployment of LLMs in medicine and healthcare. Electronic databases and preprint servers were queried using a comprehensive search strategy. 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. Four fields of applications emerged and testify to a vivid exploration phase. Advantages of using LLMs are attributed to their capacity in data analysis, personalized information provisioning, support in decision-making, mitigating information loss and enhancing information accessibility. However, we also identifies recurrent ethical concerns connected to fairness, bias, non-maleficence, transparency, and privacy. A distinctive concern is the tendency to produce harmful misinformation or convincingly but inaccurate content. A recurrent plea for ethical guidance and human oversight is evident. Given the variety of use cases, it is suggested that the ethical guidance debate be reframed to focus on defining what constitutes acceptable human oversight across the spectrum of applications. This involves considering diverse settings, varying potentials for harm, and different acceptable thresholds for performance and certainty in healthcare. In addition, a critical inquiry is necessary to determine the extent to which the current experimental use of LLMs is necessary and justified.

• The paper highlights that deploying LLMs like ChatGPT in healthcare is a complex social experiment requiring nuanced ethical guidance, human oversight tailored to context, and iterative learning.
• Key ethical concerns identified across clinical, patient, professional, and public health applications include data bias leading to inequalities, risks of misinformation, loss of research integrity, and challenges related to oversight and commercial interests.
• Addressing these issues requires redefining ethical guidance with context-aware human oversight, establishing robust guidelines and validation mechanisms, and acknowledging LLM deployment as a complex social experiment.

Ethical Dimensions of ChatGPT Deployment in Medicine and Healthcare

The paper "The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on LLMs" presents a rigorous exploration into the ethical implications of deploying LLMs, particularly ChatGPT, within the healthcare domain. Given the significant attention LLMs have garnered since the release of ChatGPT, this systematic review serves as a critical analysis of the ethical concerns and practical applications currently being investigated in medicine.

The methodology employed in this paper involved a modified rapid review approach, which screened 796 records from electronic databases and preprint servers, ultimately identifying 53 relevant papers for meta-aggregative synthesis. The focus was placed on delineating practical applications of LLMs in healthcare and the ethical issues they entail. From the synthesis of these records, four major fields emerged: clinical applications, patient support, professional support, and public health perspectives.

Key Findings

1. Clinical Applications: The review highlights LLM's potential to enhance predictive analysis, risk assessment, patient consultation, diagnosis, and treatment planning. These applications promise improved efficiency in healthcare delivery, yet they raise ethical issues such as biases from training data, potentially exacerbating existing inequalities. The use of LLMs for diagnosis underscores concerns over accuracy, interpretability, and the erosion of clinical authority due to potential misinformation or biased outputs.
2. Patient Support Applications: In patient-facing contexts, LLMs can facilitate timely access to medical information, enhancing health literacy and empowering patient autonomy. However, ethical concerns are significant when laypersons utilize these systems given the risk of misinformation and lack of situational awareness, which could lead to harm. This is particularly problematic in settings lacking adequate guidelines and regulatory oversight.
3. Professional Support and Research: The paper discusses the potential for LLMs to streamline documentation and administrative tasks, relieve healthcare professionals of mundane burdens, and accelerate research workflows. However, concerns persist regarding the introduction of biases, loss of research integrity, and the need for stringent oversight to counter these challenges. The commercial nature of LLMs further complicates these dimensions by limiting accessibility and fostering power concentration.
4. Public Health Perspectives: There are promising applications in public health campaigns and monitoring health trends. Nonetheless, the risk of dual use—including the dissemination of false information—necessitates caution, particularly as the potential for AI-driven infodemics looms. The economic model governing LLM development, dominated by commercial interests, presents additional ethical considerations around accessibility and fairness.

Implications and Future Directions

The insights from this systematic review underscore the necessity for a nuanced ethical discourse tailored to the diverse applications and users of LLMs in healthcare. The authors propose redefining the debate on ethical guidance to focus on acceptable human oversight levels across different contexts. This involves contemplating the varying epistemic positions of users—from experts to laypersons—and ensuring human oversight aligns with principles of healthcare ethics.

Furthermore, recognizing the implementation of LLMs as akin to a "social experiment" could inform future ethical discourses, emphasizing the need for iterative learning and uncertainty reduction as we navigate the complex implications of these technologies. The establishment of ethical limits, especially in a domain as sensitive as healthcare, alongside robust guidelines and validation mechanisms, will be crucial.

In summary, while the paper illustrates the potential promise of LLMs, it simultaneously calls for caution. The ethical challenges delineated warrant comprehensive strategies and research to ensure LLM applications contribute positively to healthcare without compromising ethical principles. The dialogue at the intersection of AI and healthcare ethics must remain vigilant, adaptive, and informed by ongoing developments.