ETHICAL AI
Season 1, Episode 11
This new article from Dr. Shauly and his research team addresses the opportunities and inherent risks of integrating generative AI (genAI) technologies like large language models into surgical care. They propose five ethical principles, including data transparency, patient autonomy, safety and accountability, equity, and sustainability, which are adapted from World Health Organization guidelines to govern genAI adoption in plastic surgery. The article details ethical challenges such as algorithmic bias, lack of transparency in AI decision-making, and the potential for patient data breaches, emphasizing that no genAI has yet received Food and Drug Administration (FDA) approval for surgical use. Through hypothetical scenarios and a code of conduct, the text stresses the importance of rigorous testing, informed consent, and human oversight to ensure genAI ethically enhances, rather than compromises, patient trust and care.
Comprehensive Study Guide
Short Answer Questions
Instructions: Please answer the following questions in 2-3 sentences each.
1. What are the five ethical principles, adapted from World Health Organization recommendations, proposed to guide the adoption of genAI in plastic surgery?
2. Explain the concept of "explainable AI" and describe its two primary forms as discussed in the text.
3. What is the "responsibility gap" in the context of genAI-augmented clinical decision-making, and what concern does it raise?
4. According to the text, how does the evolving nature of genAI complicate the principle of informed consent for patients?
5. Define "AI hallucination" and explain why it poses a significant risk in a surgical context.
6. What is the current regulatory status of genAI technology for surgical use in the United States, as stated in the article?
7. How can the use of genAI potentially perpetuate or worsen health disparities in plastic surgery?
8. Distinguish between autonomous and nonautonomous AI tools, providing an example of each from the healthcare field as mentioned in the source.
9. What are some of the key risks associated with direct-to-patient AI communications, such as chatbots?
10. The article mentions a trade-off between full explainability and accuracy in genAI models. What is this trade-off, and what example is used to illustrate that explainability is not always a prerequisite for efficacy?
Short Answer Key
1. The five proposed ethical principles are: ensuring data transparency and intelligibility; maintaining patient autonomy; prioritizing safety and accountability (including efficacy and responsibility); promoting equity and inclusivity; and investing in sustainability and adaptability. These principles are intended to guide the safe, effective, and equitable integration of genAI into plastic surgery.
2. Explainable AI refers to the goal of making AI decisions understandable, which helps build physician-patient trust and informs surgeons. Its two forms are inherent explainability, which applies to models with clear, quantifiable input-output relationships (like a predictive linear regression), and post hoc explainability, which is used for complex models to reverse-engineer and dissect the decision-making process.
3. The "responsibility gap" refers to the ambiguity of who is liable for harm caused by an ill-informed genAI decision, especially since genAI uses unsupervised learning not directly coded by engineers. This places a significant burden on surgeons or healthcare workers who use the technology but did not develop it and may not understand its internal logic.
4. GenAI evolves by learning from new data, which means patient information can be reused in ways not originally consented to or expected by the patient. As AI capabilities expand over time, patients may not be fully aware of how their data is being repurposed, which complicates the management of informed consent.
5. AI hallucination is a phenomenon where chatbots produce incorrect responses that are logical, well-defined, plausible, and articulated with confidence. This is dangerous in a surgical setting because it may lead to inflated trust in the technology, potentially resulting in catastrophic consequences if a surgeon follows the flawed advice.
6. To date, no generative AI (genAI) technology has been reviewed and approved by the US Food and Drug Administration (FDA) for use in any surgical domain. While the FDA has approved six AI medical products labeled as plastic surgery devices, none of these are genAI or Large Language Models (LLMs).
7. GenAI can perpetuate health disparities if its training data underrepresents diverse and vulnerable groups, leading to models that reinforce social stereotypes and biases. For example, GPT-4 has been documented exhibiting racial and gender bias in medical recommendations and exaggerating disease prevalence disparities, which could lead to inequitable care if adopted on a large scale.
8. Autonomous AI tools can make diagnostic decisions without human interpretation, such as a system that detects large vessel occlusions in stroke patients and alerts specialists directly. Nonautonomous AI agents, like ChatGPT and Bard, serve as assistants for surgeons and require a prompt to function; they are designed to supplement, not replace, human judgment.
9. Direct-to-patient AI communication risks disseminating misinformation or impersonalized medical advice that lacks the nuance a clinician provides. This can lead to patients misunderstanding instructions or following incorrect advice, especially if they are unaware the guidance is AI-driven and not from a surgeon.
10. The trade-off is that in achieving full explainability for a complex genAI model, researchers might over-simplify it, resulting in a loss of accuracy. The article uses acetaminophen as an example of efficacy without full explainability, noting that despite its extensive use, the precise mechanisms of its therapeutic effects remain unclear.
Key Terms
AI Hallucination: The production of logical, well-defined, and plausible incorrect responses by chatbots like LLMs. These counterfactual outputs are often articulated with confidence.
Algorithm Life Cycle: The entire process of an AI model’s existence, including diverse data collection, development, trials, and validation, which should incorporate input from varied backgrounds to ensure equity.
Autonomous AI: AI-based tools capable of analyzing data and providing diagnostic decisions without the need for human interpretation. An example is a system that diagnoses diabetic retinopathy directly from retinal images.
Explainable AI: A concept in which the decisions made by AI can be justified and made intelligible to users, driven by the need to ensure physician-patient trust and safety. It takes two forms: inherent and post hoc.
Generative AI (genAI): A subfield of Artificial Intelligence (AI) that uses large language models (LLMs) to generate realistic images, text, and videos to assist in automating tasks.
Inherent Explainability: A form of explainability pertaining to AI models with clear input-output data where the relationships between independent and dependent variables can be quantified, such as in a predictive linear regression.
Janus Interface: A technique involving the modification of a Large Language Model (LLM) that allows users to circumvent security measures and potentially access confidential, personally identifiable information.
Large Language Models (LLMs): The underlying technology for genAI systems like ChatGPT, Bard, and Llama. They can efficiently handle complex concepts and provide multi-modal responses to a wide array of inquiries and prompts.
Nonautonomous AI: AI agents, such as ChatGPT and Bard, that are prompted to serve as assistants for surgeons. They are designed to assist, not replace, human decision-makers.
Post Hoc Explainability: A method used for complex AI models that lack simple input-output relationships. It aims to dissect the model’s decision-making process by using a confluence of decision variables to “reverse-engineer” the output.
Responsibility Gap: A situation where it is unclear who would be held liable for ill-informed or harmful clinical decisions augmented by genAI, potentially placing the burden on surgeons who did not develop the technology.
Software-As-A-Medical-Device (SAAMD): The category under which the FDA regulates AI and machine learning technologies. This regulatory strategy focuses on continuous monitoring and evaluation of real-world performance.
Technological Divide: A situation where populational inequities in healthcare are exacerbated by advancements in medical technology, creating disparities in access and outcomes.
