This month we would like to spotlight two articles focused on the applications of artificial intelligence technologies in healthcare. In an article in Machine Learning and Knowledge Extraction – Evaluation of AI ChatBots for the Creation of Patient-Informed Consent Sheets – Raimann et al. assessed the ability of large language models (LLMs) to generate information sheets for six basic anesthesiologic procedures.The authors found that that the three LLMs tested fulfilled less than 50% of the predetermined requirements for a satisfactory and compliant information sheet. They also found that the descriptions of key elements such as risks and documentation regarding consultation varied. The authors assess that LLMs have “clear limitations” in generating patient information sheets.
Park addresses the patient perspective on AI use in healthcare provision in the Digital Health article – Patient perspectives on informed consent for medical AI: A web-based experiment. Through this work Park adds a new voice to the debate about whether, when using LLMs as a decision aid, healthcare providers ought to disclose this to the patients. It was found that patients trust second opinions from other physicians more than an AI diagnosis, but as the risk level increased for procedures, as did the importance of AI generated information. This study found the disclosure of AI use in diagnosis to be necessary from a patient perspective.
The Center for Informed Consent Integrity is exploring use of ChatGPT 4.0 to analyze the informed consent landscape, specifically how the 60+ editions of this digest might function as a specific content base for inquiry. We have encountered some limitations given our purpose, albeit different than those found by the authors below. We will continue to explore generative AI to strengthen our work and keep our readers updated.
Evaluation of AI ChatBots for the Creation of Patient-Informed Consent Sheets
Florian Jürgen Raimann, Vanessa Neef, Marie Charlotte Hennighausen, Kai Zacharowski, Armin Niklas Flinspach
Machine Learning and Knowledge Extraction, 24 May 2024
Abstract
Introduction
Large language models (LLMs), such as ChatGPT, are a topic of major public interest, and their potential benefits and threats are a subject of discussion. The potential contribution of these models to health care is widely discussed. However, few studies to date have examined LLMs. For example, the potential use of LLMs in (individualized) informed consent remains unclear.
Methods
We analyzed the performance of the LLMs ChatGPT 3.5, ChatGPT 4.0, and Gemini with regard to their ability to create an information sheet for six basic anesthesiologic procedures in response to corresponding questions. We performed multiple attempts to create forms for anesthesia and analyzed the results checklists based on existing standard sheets.
Results
None of the LLMs tested were able to create a legally compliant information sheet for any basic anesthesiologic procedure. Overall, fewer than one-third of the risks, procedural descriptions, and preparations listed were covered by the LLMs.
Conclusions
There are clear limitations of current LLMs in terms of practical application. Advantages in the generation of patient-adapted risk stratification within individual informed consent forms are not available at the moment, although the potential for further development is difficult to predict.
Patient perspectives on informed consent for medical AI: A web-based experiment
Hai Jin Park
Digital Health, 30 April 2024
Abstract
Objective
Despite the increasing use of AI applications as a clinical decision support tool in healthcare, patients are often unaware of their use in the physician’s decision-making process. This study aims to determine whether doctors should disclose the use of AI tools in diagnosis and what kind of information should be provided.
Methods
A survey experiment with 1000 respondents in South Korea was conducted to estimate the patients’ perceived importance of information regarding the use of an AI tool in diagnosis in deciding whether to receive the treatment.
Results
The study found that the use of an AI tool increases the perceived importance of information related to its use, compared with when a physician consults with a human radiologist. Information regarding the AI tool when AI is used was perceived by participants either as more important than or similar to the regularly disclosed information regarding short-term effects when AI is not used. Further analysis revealed that gender, age, and income have a statistically significant effect on the perceived importance of every piece of AI information.
Conclusions
This study supports the disclosure of AI use in diagnosis during the informed consent process. However, the disclosure should be tailored to the individual patient’s needs, as patient preferences for information regarding AI use vary across gender, age and income levels. It is recommended that ethical guidelines be developed for informed consent when using AI in diagnoses that go beyond mere legal requirements.
Center for Informed Consent Integrity 