Democratizing Subspecialty Expertise in Cardiology with AMIE: An AI-Assisted Approach to Diagnosing Inherited Cardiovascular Diseases

Table of Contents

Overall Summary

Overview

This research explores the potential of AMIE (Articulate Medical Intelligence Explorer), an AI system, to address the global shortage of subspecialist medical expertise, particularly in complex cardiology cases like hypertrophic cardiomyopathy (HCM). Using a real-world dataset of 204 complex cases from a subspecialist cardiology practice at Stanford, the study compares AMIE's performance to that of three general cardiologists in diagnosing and managing rare cardiac diseases. The study employs a blinded, counterbalanced reader study design, where both AMIE and cardiologists independently assess cases, followed by cardiologists revising their assessments after reviewing AMIE's output. Four subspecialist cardiologists then evaluate all responses using a ten-domain rubric, comparing AMIE's performance with general cardiologists and assessing the impact of AMIE assistance on cardiologist responses. The study aims to evaluate AMIE's diagnostic capabilities, its potential as an assistive tool, and its implications for democratizing access to specialized medical knowledge.

Key Findings

Strengths

Areas for Improvement

Significant Elements

Figure 6

Description: Figure 6 compares AMIE's performance to general cardiologists across ten domains and five individual assessment questions. It visually represents AMIE's superiority in certain domains, equivalence in others, and the different error profiles of AMIE (more extra content and errors) and cardiologists (more omissions and inapplicability).

Relevance: This figure directly visualizes the key findings of the study, allowing for a quick and clear understanding of AMIE's performance compared to human clinicians.

Table A.3

Description: Table A.3 quantifies the improvement in cardiologist responses after accessing AMIE's output, showing a substantial increase in preference for assisted responses across all domains, notably a 60.3% increase for the entire response. This table demonstrates the significant positive impact of AMIE assistance on clinical decision-making.

Relevance: This table provides crucial quantitative evidence supporting AMIE's effectiveness as an assistive tool, demonstrating its potential to improve the quality of care provided by general cardiologists.

Conclusion

This study demonstrates the potential of AMIE, an LLM-based AI system, to assist general cardiologists in diagnosing and managing complex inherited cardiovascular diseases. AMIE performed comparably to general cardiologists in overall assessments and even outperformed them in certain domains, particularly those involving genetic information. Importantly, access to AMIE's responses significantly improved cardiologist performance across all evaluated areas, by 60.3% for the overall response and by varying degrees for other domains. However, AMIE also exhibited a higher rate of clinically significant errors, primarily related to over-testing, which necessitates careful consideration and further refinement before clinical implementation. Future research should focus on incorporating multimodal data, including images and patient-reported outcomes, expanding the dataset to diverse populations and languages, addressing ethical implications, and developing strategies to mitigate AMIE's tendency toward over-testing while preserving its strengths in comprehensive assessment and genetic information integration. These advancements could pave the way for democratizing access to subspecialist expertise, improving the quality of care for patients with rare and complex cardiac conditions, especially in underserved areas with limited access to specialists.

Section Analysis

Towards Democratization of Subspeciality Medical Expertise

Overview

This section introduces the title of the research paper, "Towards Democratization of Subspeciality Medical Expertise," and lists the authors and their affiliations. It focuses on the problem of limited access to subspecialist medical expertise, particularly in rare and complex diseases like hypertrophic cardiomyopathy (HCM), and proposes exploring the potential of AMIE, an AI system, to improve clinical decision-making in cardiology.

Key Aspects

Strengths

Suggestions for Improvement

Abstract

Overview

This research investigates the use of AMIE, an AI system, to address the shortage of subspecialist medical expertise, particularly in complex cardiology cases. Using a real-world dataset and a ten-domain evaluation rubric, the study compares AMIE's performance to general cardiologists in diagnosing and managing rare cardiac diseases. The findings suggest that AMIE outperforms general cardiologists in certain areas and can significantly improve their diagnostic abilities when used as an assistive tool.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

This section introduces the global shortage of specialized medical expertise, particularly in rare and complex diseases, using hypertrophic cardiomyopathy (HCM) as a key example in cardiology. It emphasizes the severe consequences of delayed or absent access to specialists, such as increased morbidity and mortality. The introduction then proposes large language models (LLMs) as a potential solution to improve access to specialized knowledge and highlights the need for rigorous assessment of their capabilities in specific medical fields.

Key Aspects

Strengths

Suggestions for Improvement

Methods

Overview

This section details the study's methodology, which involved a blinded, counterbalanced reader study to evaluate AMIE's ability to diagnose, triage, and manage patients with suspected inherited cardiovascular disease. The study used data from 204 real-world patients at the Stanford Center for Inherited Cardiovascular Disease (SCICD), including various cardiac test results and genetic information. Three general cardiologists and AMIE independently assessed the cases, with the cardiologists later revising their assessments after reviewing AMIE's output. Subspecialist cardiologists then evaluated all responses using a rubric.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure 1

Figure 1 illustrates the study design using a flow diagram. It shows how patient data from various sources (genetic tests, ECGs, ambulatory cardiac monitors) is used by both the AI system, AMIE, and general cardiologists to make assessments. These assessments are then evaluated by subspecialist cardiologists using a 10-criteria rubric. The flow diagram clarifies the process of data collection, assessment, and evaluation, highlighting the role of AMIE in assisting diagnosis and management of cardiovascular disease.

First Mention

Text: "Figure 1 | Study design."

Context: This study probes the potential of LLMs to democratize subspecialist-level expertise by focusing on an indicative example, the domain of genetic cardiomyopathies like HCM. Our key contributions are as follows: [List of contributions] Figure 1 | Study design.

Relevance: This figure is crucial for understanding how the study was conducted and how AMIE's performance was compared to that of human cardiologists. It visually represents the flow of information and the different stages of evaluation.

Critique
Visual Aspects
  • The figure could benefit from clearer labeling of the arrows to indicate the direction of information flow.
  • Using different colors or shapes for the 'AMIE' and 'Cardiologist' boxes could improve visual distinction.
  • Adding a legend explaining the different data sources (e.g., ECG, genetic test) would enhance clarity.
Analytical Aspects
  • The figure could include a brief explanation of the 10-domain evaluation rubric used by the subspecialists.
  • The figure could visually represent the two stages of cardiologist assessment: initial and AMIE-assisted.
  • The figure could highlight the blinding process to emphasize the objectivity of the evaluation.
Numeric Data
figure 2

Figure 2 visually represents the architecture of the AMIE system. It shows a cyclical process involving four key components: Clinical History, Medical Reasoning, Medical Knowledge, and Diagnostic Dialogue. These components interact in a loop, where clinical history informs medical reasoning, which draws upon medical knowledge to generate a diagnostic dialogue. This dialogue then feeds back into the clinical history, allowing for iterative refinement of the diagnosis.

First Mention

Text: "Figure 2 | AMIE architecture."

Context: The assessment of patients involves review of the patient’s history and review of tests such as cardiac MRIs, rest and stress echocardiograms, cardiopulmonary stress tests, ECGs, ambulatory Holter monitors, and genetic testing. [...description of data and model...] (see Figure 2).

Relevance: This figure is essential for understanding how AMIE works and how it processes information to arrive at a diagnosis. It explains the system's core components and their interaction, providing insight into its diagnostic process.

Critique
Visual Aspects
  • The figure could use more descriptive labels within each component box to explain their function in more detail.
  • The arrows could be labeled to indicate the specific type of information being exchanged between components.
  • Using different colors or visual cues for each component could enhance visual clarity.
Analytical Aspects
  • The figure could include a brief explanation of the underlying technology used in each component, such as the type of LLM or knowledge base.
  • The figure could illustrate how external tools like web search are integrated into the process.
  • The figure could show how the self-critique mechanism works within the system.
Numeric Data
figure 2

Figure 2 illustrates the development and specialization of AMIE, an AI model for medical diagnosis. Part (a) shows how AMIE was initially trained using a simulated environment where it learned through conversations between simulated patients and doctors. Think of it like a student doctor practicing with actors playing patients. This training helps AMIE learn how to ask questions, understand symptoms, and make diagnoses. Part (b) shows how AMIE was then tested using real patient data. Out of 213 cases, 9 were used to figure out the best way to give information to AMIE and get answers from it. The remaining cases were used to compare AMIE's performance to that of human cardiologists. The cardiologists first diagnosed the cases on their own, then they got to see AMIE's diagnoses and could change their own answers if they wanted. Finally, specialist cardiologists compared the diagnoses from AMIE and the human cardiologists.

First Mention

Text: "Figure 2 | a) Development of AMIE. AMIE was trained with a self-play based simulated learning environment (see [9] for details)."

Context: Describes the development and evaluation of AMIE using real patient data and comparison with cardiologists.

Relevance: This figure is crucial for understanding how AMIE was developed and evaluated, showing the progression from simulated training to real-world application and comparison with human experts. It highlights the methodology used to assess AMIE's performance in a specialized medical domain.

Critique
Visual Aspects
  • The figure is complex and could be simplified for better clarity. Part (a) could use clearer icons or visuals to represent the different components of the training environment.
  • The connection between parts (a) and (b) could be made more explicit visually.
  • The text within the figure is small and difficult to read.
Analytical Aspects
  • The figure could benefit from a clearer explanation of the 'prompting and inference strategy' mentioned in the caption.
  • The caption could specify the types of ratings and preferences provided by the subspecialist cardiologists.
  • The figure doesn't explain how the 9 cases used for iteration were different from the 204 test cases.
Numeric Data
  • Total Cases: 213
  • Iteration Cases: 9
  • Test Cases: 204
figure 3

Figure 3 shows the assessment form used by both the AI (AMIE) and the cardiologists in the study. It's like a quiz they both had to take about each patient case. The form has sections for their overall impression of the case, whether they think the patient has a genetic heart condition, and whether the patient should see a specialist. It also asks for their diagnosis, how they would manage the patient, and how genetic test results (if available) would change their answers. Imagine it as a structured way to get everyone's medical opinion on the same set of information.

First Mention

Text: "Figure 3 | Assessment Form for AMIE/cardiologist responses to cases."

Context: Describes the assessment form used by AMIE and cardiologists to evaluate patient cases.

Relevance: This figure is essential for understanding how the AI and cardiologists' performance was evaluated. It provides a detailed breakdown of the criteria used to assess their diagnostic abilities and management plans. It ensures a fair comparison by providing a standardized format for their responses.

Critique
Visual Aspects
  • The figure could be more visually engaging, perhaps by using different colors or fonts to highlight the different sections.
Analytical Aspects
  • The form could include a section for the rationale behind the diagnosis and management plan, providing insights into the reasoning process.
  • The form could specify the types of genetic tests considered and how their results are interpreted.
  • The form could be made more interactive for online use, allowing for direct input and automated analysis.
Numeric Data
figure 4

This figure presents the evaluation form used by subspecialist cardiologists to compare responses from AMIE and general cardiologists. It lists ten criteria for comparison, including overall impression, consult question, triage assessment, diagnosis, management, and the impact of genetic test results. For each criterion, the subspecialists had to choose which response they preferred (Response 1, Tie, or Response 2). This form allows for a direct, pairwise comparison across different aspects of the responses, enabling a detailed assessment of the strengths and weaknesses of each.

First Mention

Text: "Subspecialist cardiologists from the Stanford Center for Inherited Cardiovascular Disease provided individual ratings (Figure 5) and direct preferences (Figure 4) between AMIE and cardiologists, and between the cardiologist responses with and without assistance from AMIE."

Context: This sentence, found in the 'Model Development' subsection, introduces the two evaluation forms used by subspecialist cardiologists. It mentions Figure 4, the preference evaluation form, and Figure 5, the individual evaluation form, highlighting their role in the study's evaluation process.

Relevance: This figure is crucial for understanding how the researchers compared the performance of AMIE and general cardiologists. It provides a structured framework for evaluating different aspects of their responses, allowing for a detailed and nuanced comparison. By focusing on specific domains, the form helps pinpoint the areas where AMIE excels or falls short compared to human experts.

Critique
Visual Aspects
  • The form is clear and easy to read, with distinct sections for each criterion.
  • The use of a simple 'Response 1, Tie, Response 2' format makes the comparison straightforward.
  • The numbering of the criteria ensures a systematic evaluation.
Analytical Aspects
  • The criteria cover a broad range of relevant clinical aspects, from overall impression to the impact of genetic test results.
  • The direct comparison format allows for clear differentiation between the two sets of responses.
  • The inclusion of a 'Tie' option acknowledges the possibility of equivalent performance.
figure 5

This figure shows the individual evaluation form used by subspecialist cardiologists to assess the responses from both AMIE and general cardiologists independently. The form consists of five yes/no questions focusing on clinically significant errors, the presence of unnecessary content, the omission of important content, evidence of correct reasoning, and the applicability of the response to specific medical demographics. This individual evaluation complements the direct comparison in Figure 4, providing a more granular assessment of the quality and potential biases in each response.

First Mention

Text: "Subspecialist cardiologists from the Stanford Center for Inherited Cardiovascular Disease provided individual ratings (Figure 5) and direct preferences (Figure 4) between AMIE and cardiologists, and between the cardiologist responses with and without assistance from AMIE."

Context: This sentence, found in the 'Model Development' subsection, introduces the two evaluation forms used by subspecialist cardiologists. It mentions Figure 5, the individual evaluation form, and Figure 4, the preference evaluation form, highlighting their role in the study's evaluation process.

Relevance: This figure is essential for understanding the detailed evaluation process used in the study. It provides insights into the specific criteria used to assess the quality and potential biases of both AMIE and general cardiologist responses. By examining these individual assessments, the researchers could identify specific strengths and weaknesses of each, going beyond the simple preference comparison in Figure 4.

Critique
Visual Aspects
  • The form is concise and easy to understand, with clear yes/no questions.
  • The numbering of the questions ensures a systematic evaluation.
  • The layout is simple and uncluttered.
Analytical Aspects
  • The questions address important aspects of response quality, including errors, omissions, reasoning, and potential biases.
  • The yes/no format allows for a quick and efficient evaluation.
  • The focus on individual assessment complements the pairwise comparison in Figure 4.

Results

Overview

This section presents the findings of the study comparing the performance of AMIE, an AI system, with general cardiologists in assessing patients with suspected inherited cardiovascular disease. AMIE was found to be superior to general cardiologists in five out of ten domains, including explaining the rationale for suspecting a genetic heart condition, providing additional patient and test information, suggesting management plans, and explaining genetic test results. When cardiologists had access to AMIE's responses, they significantly improved their assessments in almost all cases. While AMIE was more thorough and sensitive, it also had a higher rate of clinically significant errors, often related to suggesting unnecessary tests. General cardiologists, on the other hand, were more concise but sometimes missed crucial information.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

table 1

Table 1 presents an overview of the patient demographics and data availability for different clinical tests. The average age of the 204 patients was 59, with the youngest being 18 and the oldest 96. The table then lists various cardiac tests, like Cardiac MRI (CMR) and electrocardiogram (ECG), and shows how many patients had data available for each test. For example, 121 patients (59.3%) had CMR data, while 188 (92.2%) had ECG data. This information is important because it shows the types and amount of data used to evaluate AMIE and the cardiologists, giving us an idea of how complete the information was for each patient.

First Mention

Text: "Table 1 | Clinical text data availability across patients."

Context: The number and percentage of patients with available clinical text data for each test was as follows: CMR: 121 (59.3%), CPX: 115 (56.4%), resting TTE: 172 (84.3%), exercise TTE: 131 (64.2%), ECG: 188 (92.2%), ambulatory holter monitor: 151 (74.0%), and genetic testing: 147 (72.0%) (see Table 1).

Relevance: This table is important because it provides context for the study's results. It tells us about the patients included in the study, their ages, and what kind of medical test data was available for analysis. This helps us understand the scope of the study and the limitations of the data.

Critique
Visual Aspects
  • The table is clearly organized and easy to read.
  • The use of percentages alongside raw numbers makes it easy to understand the data distribution.
  • The table could benefit from a clearer title, perhaps specifying that it represents 'Patient Demographics and Data Availability'.
Analytical Aspects
  • The table could include the standard deviation for the age to provide a better understanding of the age distribution.
  • The table could be split into two separate tables: one for demographics and one for data availability, for better organization.
  • The table could include a brief explanation of why certain tests might have more missing data than others.
Numeric Data
  • Mean Age: 59.0 years
  • Age Range: 18 years
  • Age Range: 96 years
  • CMR Data Available: 121
  • CPX Data Available: 115
  • Resting TTE Data Available: 172
  • Exercise TTE Data Available: 131
  • ECG Data Available: 188
  • Holter Monitor Data Available: 151
  • Genetic Testing Data Available: 147
figure 6

Figure 6 compares AMIE's performance to that of general cardiologists. Part (a) shows which one was preferred by subspecialist cardiologists across 10 different areas, like explaining the consult question or the management plan. Think of it as a head-to-head matchup where AMIE wins in 5 areas, ties in the rest, and never loses. Part (b) looks at how AMIE and the cardiologists did on 5 individual questions, such as whether they made any errors or missed important information. It shows, for example, that AMIE was more likely to include extra information and make errors, while cardiologists were more likely to give answers that didn't apply to certain patients.

First Mention

Text: "Figure 6 | a) Preference between AMIE and cardiologist responses."

Context: The domains in which AMIE responses were preferred were: ‘consult question explanation’, ‘additional patient information’, ‘additional test information’, ‘management’, and ‘genetic explanation’. Figure 6 |

Relevance: This figure is central to the study's results, directly comparing AMIE's performance to human cardiologists. It visually represents the key findings, showing where AMIE excels and where it needs improvement. This information is crucial for understanding the potential of AMIE as a clinical tool and for identifying areas for future development.

Critique
Visual Aspects
  • Part (a) could use clearer labels for the 10 domains to make them easier to understand.
  • Part (b) could use different colors for the bars representing AMIE and cardiologists to improve visual distinction.
  • The figure could benefit from a more descriptive caption, explaining the meaning of 'preferred' and the individual assessment questions.
Analytical Aspects
  • The figure could include error bars or confidence intervals to show the statistical significance of the differences.
  • Part (a) could show the magnitude of the preference, not just which one was preferred.
  • Part (b) could provide more context for the individual assessment questions, explaining what each question measures.
Numeric Data
figure 7

Figure 7 is a bar chart comparing cardiologists' responses with and without the help of AMIE, an AI assistant. Imagine a doctor trying to diagnose a patient, first on their own and then after getting a second opinion from AMIE. The chart shows how often specialists preferred each type of response across 10 different areas, like the overall diagnosis, management plan, and explanation of genetic test results. Each area has three bars: one for when the cardiologist used AMIE's help (Cardiologist + AMIE), one for the cardiologist's initial response (Cardiologist Alone), and one for when the specialists couldn't decide which was better (Tie). The taller the 'Cardiologist + AMIE' bar, the more often specialists preferred the response where the cardiologist had AMIE's help. The chart also has little lines (error bars) on top of each bar, which show how much the results might vary.

First Mention

Text: "Figure 7 | Preference between cardiologist responses with and without access to AMIE’s response."

Context: Of the 204 patient assessments, 195 of the assessments (95.6%) were changed by the general cardiologists after seeing AMIE’s response. [...] Across the remaining 9 specific domains, the AMIE-assisted responses were preferred for all domains when directly compared to the general cardiologists alone, though ‘Tie’ was the most common evaluation for 8 of the 10 domains (see Figure 7 and Table A.3).

Relevance: This figure is important because it shows how much AMIE can help cardiologists improve their diagnoses and treatment plans. It directly addresses the question of whether using AI can improve the quality of care provided by general cardiologists, especially in complex cases where specialist expertise is limited.

Critique
Visual Aspects
  • The colors used for the bars could be more distinct to improve readability.
  • Labeling the y-axis with the full domain names instead of abbreviations would enhance clarity.
  • Adding a clear title that summarizes the main finding (e.g., 'AMIE Assistance Improves Cardiologist Responses') would make the chart more impactful.
Analytical Aspects
  • The caption could explain what the error bars represent (e.g., 90% confidence intervals).
  • The figure could include the p-values for each comparison to show the statistical significance of the differences.
  • The figure could benefit from a brief explanation of why 'Tie' was the most common outcome for many domains.
Numeric Data
figure 8

Figure 8 summarizes what specialist cardiologists thought about the responses from AMIE and the general cardiologists. Instead of a chart or graph, it uses text generated by another AI, Gemini 1.5 Flash, to explain the main reasons why specialists preferred one response over the other. Think of it like getting a summary of expert opinions. The specialists gave feedback on about 78% of the cases. The summary highlights that AMIE was generally praised for being thorough and considering many possible diagnoses, while the general cardiologists were seen as more concise but sometimes missed important details or jumped to conclusions too quickly. The summary also lists specific reasons why specialists preferred AMIE or the cardiologists, like AMIE's broader differential diagnosis or the cardiologists' conciseness.

First Mention

Text: "Figure 8 | LLM-generated summary of subspecialist comments for preference rating between AMIE and the cardiologists."

Context: While AMIE and cardiologists had similar overall preferences (see Figure 6), the types of feedback they each received were quite different; [...] In this way, AMIE’s assistive value could be in thorough sensitive assessments, which then can be refined by cardiologists, who tend to be more specific. [...] (see Figure 8).

Relevance: This figure provides valuable qualitative insights into the strengths and weaknesses of AMIE and general cardiologists, as perceived by specialist cardiologists. It helps explain the 'why' behind the preferences observed in the quantitative analysis, offering a deeper understanding of the AI's performance and its potential role in clinical practice.

Critique
Visual Aspects
  • While the text summary is clear, adding a visual component, such as a word cloud or a simple bar chart summarizing the frequency of different feedback themes, could make the information more engaging and easier to grasp.
Analytical Aspects
  • The summary could be more specific about the types of errors made by AMIE and the cardiologists.
  • The summary could discuss the implications of these findings for the future development and deployment of AI in cardiology.
  • The summary could explore the potential for combining the strengths of both AMIE and human cardiologists to improve diagnostic accuracy and patient care.
Numeric Data
  • Feedback Received: 159 assessment pairs
  • Total Assessments: 204 assessment pairs
  • Percentage Feedback: 77.9 %
  • General Cardiologist Omission Errors: 92 %
  • AMIE Omission Errors: 35.5 %
  • General Cardiologist Unnecessary Care Errors: 8 %
  • AMIE Unnecessary Care Errors: 64.9 %
figure 9

Figure 9 illustrates a hypothetical dialogue between AMIE and a general cardiologist, showcasing how AMIE could assist in real-world clinical scenarios. The figure is divided into four parts. Part (a) summarizes the clinical data from an echocardiogram and a Holter monitor for a patient suspected of having hypertrophic cardiomyopathy (HCM). Part (b) presents the independent assessments of the general cardiologist and AMIE based on this data. Notice how the cardiologist initially downplays the likelihood of genetic heart disease, while AMIE suggests a higher suspicion of HCM. Part (c) shows a simulated conversation where AMIE explains its reasoning to the cardiologist, highlighting key findings like left ventricular outflow tract obstruction and the possibility of asymptomatic HCM. Part (d) provides feedback from a subspecialist cardiologist, confirming AMIE's assessment and emphasizing the importance of referral to a specialized center. This figure demonstrates AMIE's potential to provide valuable insights and guide clinical decision-making, especially in cases with subtle or complex presentations.

First Mention

Text: "Figure 9 | Dialogue between AMIE and a general cardiologist."

Context: To explore potential future clinical uses of technology such as AMIE, we present four qualitative examples of how capabilities in dialogue could be utilized to communicate with patients or up-level generalists. The first hypothetical scenario in Figure 9 shows AMIE assisting a general cardiologist in the assessment of real-world clinical ECG and ambulatory Holter monitor text data (Figure 9a).

Relevance: This figure demonstrates AMIE's potential to augment the diagnostic capabilities of general cardiologists by providing a more comprehensive and nuanced assessment of complex cases. It highlights AMIE's ability to consider a broader range of possibilities, identify subtle findings, and provide clear explanations to support its recommendations. This is particularly important in cases like HCM, where early and accurate diagnosis is crucial for effective management and prevention of serious complications.

Critique
Visual Aspects
  • The figure is well-organized and easy to follow, with clear sections for the clinical data, individual assessments, dialogue, and subspecialist feedback.
  • The use of different colors or fonts for the cardiologist and AMIE's text could further enhance readability and distinguish their contributions.
  • The clinical data summary could be presented in a more visually appealing format, such as a table or a simplified graphical representation.
Analytical Aspects
  • The dialogue in part (c) could be expanded to include more detailed explanations of the medical terms and concepts, making it more accessible to a wider audience.
  • The subspecialist feedback could include specific examples of how AMIE's insights influenced the cardiologist's understanding and decision-making.
  • The figure could be accompanied by a brief discussion of the limitations of this hypothetical scenario and the need for further validation in real-world clinical settings.
Numeric Data

Discussion

Overview

This section discusses the study's findings on the ability of Large Language Models (LLMs), specifically AMIE, to assist generalists in assessing rare cardiac diseases. The study used a real-world dataset of patients with suspected inherited cardiomyopathies and a specialized evaluation rubric. Key findings include AMIE's comparable performance to general cardiologists in standalone assessments, its potential to significantly improve general cardiologists' diagnostic and management abilities when used as an assistive tool, and the different error profiles of AMIE (over-testing) and general cardiologists (omission). The discussion also highlights the limitations of the study, such as the use of text-based reports only and the lack of patient history and physical examination data, and emphasizes the need for further prospective research before clinical implementation.

Key Aspects

Strengths

Suggestions for Improvement

Conclusions

Overview

This section concludes that the research LLM-based AI system, AMIE, demonstrates potential in assisting general cardiologists with complex cases of inherited cardiomyopathies. AMIE performed comparably to general cardiologists in assessments, and even outperformed them in some areas. Importantly, access to AMIE's insights significantly improved the cardiologists' responses. However, AMIE also showed a higher rate of errors, primarily related to over-testing, highlighting the need for further research before clinical implementation.

Key Aspects

Strengths

Suggestions for Improvement

Appendix

Overview

This appendix provides supplementary information to support the main findings of the paper. It includes an example of AMIE's response to a patient case, further details on the subspecialist evaluations, summaries of their comments, an analysis of the types of errors made by AMIE and cardiologists, and additional dialogue examples illustrating potential clinical applications of AMIE.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure A.2

Figure A.2 summarizes the feedback from subspecialist cardiologists on the individual assessments of both AMIE and general cardiologists. The figure uses text summaries generated by an LLM (Gemini 1.5 Flash) to present the feedback for each of the five individual assessment questions (Figure 5). These questions cover topics like extra content, omitted content, correct reasoning, applicability to specific demographics, and clinically significant errors. The summaries are presented in separate boxes, with blue boxes for cardiologist feedback and red boxes for AMIE feedback. The figure also notes the proportion of responses that received comments for each category, indicating that not all assessments received feedback for every question.

First Mention

Text: "Figure A.2 | LLM-generated summaries of subspecialist comments to AMIE and cardiologist assessments."

Context: To understand the rationale behind the preferences and individual ratings provided by subspecialists, we analyzed the free-text comments left by subspecialists for AMIE’s and the general cardiologists’ responses. [...] We also performed a similar analysis on the 5 individual assessment criteria, finding that the subspecialists described very different and often complementary strengths and weaknesses of AMIE and cardiologists for each criteria (see Figure A.2).

Relevance: This figure provides valuable qualitative insights into the specific strengths and weaknesses of AMIE and general cardiologists, as perceived by subspecialist cardiologists. It helps explain the quantitative results by providing context and detailed feedback on different aspects of their assessments. This information is crucial for understanding the types of errors made by each and for identifying areas for improvement in both AMIE and clinical practice.

Critique
Visual Aspects
  • The figure is well-organized, with clear separation between cardiologist and AMIE feedback.
  • The use of different colors for the boxes effectively distinguishes the two sets of summaries.
  • The text within the boxes is concise and easy to read.
Analytical Aspects
  • The figure could benefit from a more detailed explanation of the five individual assessment questions (Figure 5) to provide context for the summaries.
  • The figure could include the actual subspecialist comments alongside the summaries to allow for a more in-depth analysis.
  • The figure could discuss the implications of the feedback for the future development and application of AMIE in clinical practice.
Numeric Data
figure A.3

Figure A.3 summarizes the key themes of clinically significant errors made by both AMIE and general cardiologists, as identified by subspecialist reviewers. The summary, generated by an LLM (Gemini 1.5 Flash), highlights AMIE's tendency towards over-testing, over-treatment, and misinterpretation of genetic information. On the other hand, general cardiologists were more likely to miss rarer diagnoses, perform incomplete workups, and inadequately integrate genetic information into management plans. The summary provides a concise comparison of the error profiles, suggesting that AMIE's errors are often related to excessive reliance on technology, while cardiologists' errors stem from a more conservative approach and potential unfamiliarity with rarer conditions or guidelines.

First Mention

Text: "Figure A.3 | LLM-generated summary of AMIE and the cardiologists clinically significant errors."

Context: Both AMIE and general cardiologists’ clinically significant errors are described in Figure A.3. We also performed a similar analysis on the 5 individual assessment criteria, finding that the subspecialists described very different and often complementary strengths and weaknesses of AMIE and cardiologists for each criteria (see Figure A.2).

Relevance: This figure is crucial for understanding the limitations and potential risks associated with both AMIE and current clinical practice. By highlighting the specific types of errors made by each, it informs strategies for improvement and emphasizes the need for careful consideration before implementing AI tools in real-world settings. The comparison of error profiles also sheds light on the complementary nature of AI and human expertise, suggesting potential for synergistic approaches to patient care.

Critique
Visual Aspects
  • While the text summary is informative, adding a visual component, such as a table or a chart comparing the frequency of different error types, could enhance clarity and engagement.
Analytical Aspects
  • The summary could provide more specific examples of the errors made by AMIE and cardiologists, illustrating the clinical implications of each error type.
  • The summary could discuss the potential underlying causes of these errors, such as limitations in AMIE's training data or biases in clinical practice.
  • The summary could explore strategies for mitigating these errors, such as incorporating human oversight or developing more robust AI models.
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table A.4

Table A.4 outlines the prompts given to AMIE, the AI system, in three simulated dialogue scenarios. These scenarios explore different potential clinical applications of AMIE: 1) explaining test results and diagnosis to a patient, 2) assisting a general cardiologist in deciding about specialist referral, and 3) presenting a comprehensive assessment as a specialist cardiologist. Each prompt describes a patient case and specifies the information AMIE should use and the role it should play in the conversation. For example, in the first scenario, AMIE is given echocardiogram and Holter monitor results for a 63-year-old female whose brother died from sudden cardiac death and is asked to explain these results to the patient. In the second scenario, AMIE is asked to help a general cardiologist decide whether a 54-year-old male with shortness of breath and dizziness should be referred to a specialist. In the third scenario, AMIE acts as the specialist cardiologist for a 64-year-old male and presents a complete assessment based on various test results.

First Mention

Text: "Table A.4 | Prompts for AMIE's simulated dialogue across three scenarios: (1) AMIE reaches a diagnosis and then explains it to a patient. (2) AMIE provides assistive dialogue for a general cardiologist. (3) AMIE assumes the role of the specialist cardiologist and presents an assessment."

Context: For the remaining three hypothetical scenarios, AMIE was given a prompt, akin to a 'one-line' summary of a patient (Table A.4) along with clinical data for the corresponding patient and asked to produce dialogues mirroring various potential use cases for AMIE: 1. AMIE reaches a diagnosis and then explains it to a patient (Figure A.4); 2. AMIE providing assistive dialogue for a general cardiologist (Figure A.5); 3. AMIE assumes the role of the specialist cardiologist and presents an assessment (Figure A.6).

Relevance: This table is important because it shows how AMIE's conversational abilities were tested in different clinically relevant situations. The prompts represent potential real-world applications of the AI, such as patient education, assisting general practitioners, and providing specialist consultations. By evaluating AMIE's performance in these scenarios, the researchers can assess its potential to improve communication, enhance decision-making, and increase access to specialized knowledge.

Critique
Visual Aspects
  • The table is clear and easy to understand, with distinct rows for each scenario and a clear explanation of AMIE's role.
  • The table could benefit from a more concise title, perhaps focusing on the key element: 'AMIE Dialogue Prompts'.
Analytical Aspects
  • The table could include a brief explanation of the clinical data provided to AMIE for each scenario.
  • The table could mention the specific goals or objectives of each dialogue scenario, such as providing accurate information to the patient or assisting the cardiologist in making a referral decision.
  • The table could link each scenario to the corresponding figure showing the actual dialogue (e.g., Scenario 1 - Figure A.4).
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Example model response

Overview

This appendix section provides a sample of AMIE's response to a clinical case, including the clinical data summary provided to AMIE and AMIE's response to the assessment form questions.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure A.1

Figure A.1 shows an example of AMIE's response to a clinical case. It's split into two parts: (a) Clinical Data Summary and (b) Example AMIE Response. Part (a) summarizes the important information from the patient's echocardiogram (an ultrasound of the heart) and Holter monitor (a portable device that records heart rhythm). This summary is like a cheat sheet for the AI, giving it the key facts about the patient's heart structure and rhythm. Part (b) shows AMIE's answers to the questions on the assessment form (Figure 3). This is where AMIE gives its overall impression of the case, whether it thinks the patient has a genetic heart problem, what the diagnosis is, and how the patient should be managed. It's like AMIE's version of a doctor's report.

First Mention

Text: "Figure A.1 | Example model response. a) Summaries of the clinical data provided to AMIE. b) The response provided by AMIE to the questions in Figure 3."

Context: This appendix section provides a sample of AMIE's response and is crucial for understanding its capabilities.

Relevance: This figure is crucial because it gives a concrete example of how AMIE analyzes patient data and generates a clinical assessment. It allows readers to see the actual output of the AI and understand how it applies its knowledge to a real-world case. This helps to illustrate AMIE's capabilities and evaluate its potential for clinical use.

Critique
Visual Aspects
  • The figure is clear and well-organized, with distinct sections for the clinical data and AMIE's response.
  • Using different colors or fonts for the clinical data and AMIE's response could improve visual distinction.
  • Highlighting key findings within the text summaries could make them easier to scan and understand.
Analytical Aspects
  • The figure could include a brief explanation of the medical terms used in the clinical data summary, making it more accessible to a wider audience.
  • The figure could compare AMIE's response to a gold-standard response from a subspecialist, providing a benchmark for its performance.
  • The figure could discuss the limitations of this specific example and its generalizability to other cases.
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Additional evaluation information

Overview

This appendix section provides detailed results from the subspecialist evaluations, offering further insights into the evaluation process. It includes tables showing the preference ratings between AMIE and cardiologist responses, individual assessments of both, and the preference between cardiologist responses before and after accessing AMIE's responses.

Key Aspects

Strengths

Suggestions for Improvement

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Table A.1

Table A.1 shows how often subspecialist cardiologists preferred AMIE's responses compared to general cardiologists' responses across 10 different areas of assessment. Each area, like 'Overall Impression' or 'Management,' has percentages showing how often AMIE was preferred, how often the general cardiologist was preferred, and how often it was a tie. For example, for 'Overall Impression,' AMIE was preferred 39.7% of the time, the cardiologist 32.4% of the time, and it was a tie 27.9% of the time. The table also shows the difference between AMIE and the cardiologist's preference percentages and a confidence interval (CI), which tells us how much these percentages might vary. If the difference is positive and the CI doesn't include zero, it means AMIE was significantly preferred in that area.

First Mention

Text: "Table A.1 | Preference rating between cardiologist and AMIE responses."

Context: Here we present detailed results from subspecialist evaluators including: the preference between AMIE and cardiologist responses, the individual assessment of AMIE and cardiologist responses, and the preference between cardiologist responses with and without access to AMIE’s response.

Relevance: This table is important because it shows a direct comparison between AMIE and general cardiologists, highlighting the areas where AMIE performs better, worse, or similarly. This helps evaluate AMIE's potential to assist or even replace general cardiologists in certain aspects of cardiovascular disease assessment.

Critique
Visual Aspects
  • The table is well-organized and easy to read, with clear headings and labels.
  • Using bold font for statistically significant differences helps highlight key findings.
  • The table could benefit from a clearer explanation of the confidence interval in the caption.
Analytical Aspects
  • The table could include a brief explanation of the clinical significance of each domain, making it easier for non-specialists to understand the results.
  • The table could discuss the potential reasons for AMIE's superior performance in certain domains and the areas where it needs improvement.
  • The table could explore the implications of these findings for the future development and application of AI in cardiology.
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Table A.2

Table A.2 shows how AMIE and general cardiologists performed on five individual assessment questions. These questions ask things like, 'Does the response have extra content?', 'Does it omit important content?', and 'Does it have a clinically significant error?'. The table shows the percentage of 'yes' answers for each question, for both AMIE and the cardiologists. It also shows the difference between these percentages and a confidence interval. For example, AMIE was more likely to have extra content (29.4% vs. 16.7%) and clinically significant errors (21.6% vs. 10.8%), while cardiologists were more likely to give responses that were inapplicable to certain patients (15.2% vs. 10.8%).

First Mention

Text: "Table A.2 | Individual assessment of cardiologist and AMIE responses."

Context: Here we present detailed results from subspecialist evaluators including: the preference between AMIE and cardiologist responses, the individual assessment of AMIE and cardiologist responses, and the preference between cardiologist responses with and without access to AMIE’s response.

Relevance: This table provides a more detailed look at the specific strengths and weaknesses of AMIE and general cardiologists. It goes beyond simple preference ratings and examines individual aspects of their responses, such as the presence of errors or omissions. This information is valuable for understanding the types of mistakes each makes and for identifying areas for improvement.

Critique
Visual Aspects
  • The table is clear and concise, with easy-to-understand headings and labels.
  • Using bold font for statistically significant differences helps draw attention to key findings.
  • The table could benefit from a more descriptive caption, explaining the five assessment questions in more detail.
Analytical Aspects
  • The table could include a discussion of the clinical implications of each assessment question. For example, what are the potential consequences of having extra content or omitting important information?
  • The table could explore the potential reasons for the observed differences between AMIE and cardiologists.
  • The table could discuss how these findings could inform the development of more effective AI tools for clinical use.
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table A.3

Table A.3 compares cardiologists' responses before and after they had access to AMIE's assessment. For each of the 10 domains (like 'Entire Response' or 'Diagnosis'), it shows the percentage of times the cardiologists' initial responses (Unassisted), their revised responses after seeing AMIE's assessment (Assisted), and 'Tie' (no clear preference) were chosen by subspecialist evaluators. It also shows the difference between the 'Assisted' and 'Unassisted' percentages. The table uses confidence intervals (CI) to show the range within which the true percentages likely fall. The key takeaway is that for all 10 domains, the 'Assisted' responses were preferred more often than the 'Unassisted' responses, showing that AMIE's input generally helped the cardiologists improve their assessments. In fact, for 'Entire Response', the assisted response was preferred a whopping 60.3% more often than the unassisted one!

First Mention

Text: "Figure 7 and Table A.3"

Context: Across the remaining 9 specific domains, the AMIE-assisted responses were preferred for all domains when directly compared to the general cardiologists alone, though ‘Tie’ was the most common evaluation for 8 of the 10 domains (see Figure 7 and Table A.3).

Relevance: This table is crucial because it directly shows how much AMIE's input improved the cardiologists' assessments. It quantifies the benefit of using AMIE as an assistive tool, demonstrating its potential to enhance clinical decision-making in complex cardiology cases. By comparing preferences across different domains, the table highlights the specific areas where AMIE's assistance was most impactful.

Critique
Visual Aspects
  • The table is clear and well-organized, with distinct columns for each category and confidence intervals.
  • Using bolder font or highlighting for the 'Assisted - Unassisted' column could emphasize the key finding of improvement.
  • Adding a clearer title that directly states the main takeaway (e.g., 'AMIE Assistance Improves Cardiologist Responses Across All Domains') would make the table more impactful.
Analytical Aspects
  • The table could include p-values for each comparison to show the statistical significance of the differences.
  • The table could provide a brief explanation of how the confidence intervals were calculated.
  • The table could discuss the clinical implications of the observed improvements, such as the potential for better patient outcomes or more efficient use of resources.
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  • Entire Response - Assisted Preference: 63.7 %
  • Entire Response - Unassisted Preference: 3.4 %
  • Entire Response - Tie: 32.8 %
  • Entire Response - Improvement (Assisted - Unassisted): 60.3 %

Summary of subspecialist free-text comments for individual assessments

Overview

This appendix section summarizes the free-text comments provided by subspecialist cardiologists on the individual assessments of both AMIE and general cardiologists. These comments offer qualitative insights into the strengths and weaknesses of each, complementing the quantitative preference ratings. The comments were summarized using an LLM (Gemini 1.5 Flash) and are presented in Figure A.2, separated into blue boxes for cardiologist feedback and red boxes for AMIE feedback. The figure also includes the proportion of responses that received comments for each of the five assessment criteria: Extra Content, Omits Content, Correct Reasoning, Inapplicability for particular demographics, and Clinically Significant Errors.

Key Aspects

Strengths

Suggestions for Improvement

Summary of Clinically Significant Errors

Overview

This appendix section summarizes the clinically significant errors made by both AMIE and general cardiologists, as identified by subspecialist reviewers. AMIE's errors tended to be related to over-testing and over-treatment, potentially due to an over-reliance on advanced technology. General cardiologists, on the other hand, were more prone to missing rarer diagnoses, conducting incomplete workups, and inadequately integrating genetic information into management plans, possibly reflecting a more conservative approach and less familiarity with rare conditions and guidelines.

Key Aspects

Strengths

Suggestions for Improvement

Additional dialogue examples

Overview

This appendix section presents additional dialogue examples to illustrate AMIE's potential clinical applications in communicating with patients and assisting general cardiologists. It includes three scenarios: AMIE explaining test results and diagnosis to a patient, AMIE assisting a general cardiologist with a referral decision, and AMIE acting as a specialist cardiologist presenting an assessment. These scenarios are described in Table A.4, and the dialogues, along with clinical data summaries and subspecialist feedback, are presented in Figures A.4, A.5, and A.6.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure A.4

Figure A.4 illustrates a simulated conversation between AMIE and a patient, demonstrating how AMIE can explain medical results and diagnoses in a clear and accessible way. The scenario involves a 63-year-old female patient whose brother died from sudden cardiac death. She has undergone an echocardiogram and a Holter monitor test. AMIE explains the results of these tests, using simple language and analogies to clarify complex medical terms like 'asymmetric left ventricular hypertrophy' and 'LVOT gradient'. AMIE also discusses the possibility of hypertrophic obstructive cardiomyopathy (HOCM) and explains the condition, its potential seriousness, and possible treatment options. The figure includes the scenario and clinical data summary, the example dialogue, and feedback from a subspecialist cardiologist, who notes AMIE's clear explanations and appropriate recommendations but also points out areas for improvement, such as discussing genetic testing and family screening.

First Mention

Text: "Figure A.4 | AMIE communicates with a patient about her diagnosis."

Context: Here we present dialogue examples to illustrate potential clinical applications of AMIE in conveying information to patients and assisting general cardiologists in their assessment. The example scenarios used to generate these dialogues are described in Table A.4, with summaries of the provided clinical data, resulting dialogues, and sub-specialist commentary for each scenario presented in Figure A.4, Figure A.5, and Figure A.6.

Relevance: This figure demonstrates AMIE's potential to enhance patient understanding and engagement by providing clear and accessible explanations of medical information. This is particularly important in complex cases like potential inherited cardiomyopathies, where patients may be anxious and overwhelmed by medical jargon. AMIE's ability to communicate effectively with patients could improve their adherence to treatment plans and overall satisfaction with care.

Critique
Visual Aspects
  • The dialogue format is effective in presenting the conversation between AMIE and the patient.
  • Using different colors or fonts for AMIE and the patient's text could improve readability.
  • The clinical data summary could be presented more visually, perhaps using icons or a simplified diagram.
Analytical Aspects
  • The dialogue could include more explicit discussion of the uncertainty associated with medical diagnoses and the need for further testing.
  • The subspecialist feedback could be more specific, providing examples of how AMIE's communication could be improved.
  • The figure could discuss the limitations of this simulated scenario and the need for validation in real-world patient interactions.
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figure A.5

Figure A.5 shows a simulated dialogue between AMIE and a general cardiologist, illustrating how AMIE can assist in clinical decision-making. The scenario involves a 54-year-old male patient with shortness of breath and dizziness. The cardiologist has ordered several tests, including an echocardiogram, stress test, Holter monitor, and cardiac MRI. AMIE discusses the results of these tests with the cardiologist, explaining the possibility of rare genetic conditions like arrhythmogenic right ventricular cardiomyopathy (ARVC) and left ventricular noncompaction (LVNC). AMIE provides brief explanations of these conditions and recommends referral to a specialized center. The figure includes the scenario and clinical data summary, the example dialogue, and feedback from a subspecialist, who notes AMIE's helpful explanations and appropriate referral recommendation but also suggests areas for improvement, such as providing more detailed information about the genetic basis of these conditions.

First Mention

Text: "Figure A.5 | AMIE assists a general cardiologist."

Context: Here we present dialogue examples to illustrate potential clinical applications of AMIE in conveying information to patients and assisting general cardiologists in their assessment. The example scenarios used to generate these dialogues are described in Table A.4, with summaries of the provided clinical data, resulting dialogues, and sub-specialist commentary for each scenario presented in Figure A.4, Figure A.5, and Figure A.6.

Relevance: This figure demonstrates AMIE's potential to support general cardiologists in managing complex cases by providing access to specialized knowledge and facilitating informed decision-making. This is particularly relevant in situations where access to subspecialists is limited or delayed. AMIE's ability to explain rare conditions and recommend appropriate referrals could improve the quality and timeliness of care for patients with complex cardiac conditions.

Critique
Visual Aspects
  • The dialogue format effectively presents the interaction between AMIE and the cardiologist.
  • Using different colors or fonts for AMIE and the cardiologist's text could improve readability.
  • The clinical data summary could be presented in a more visually engaging format, such as a table or a timeline.
Analytical Aspects
  • The dialogue could include more detailed explanations of the diagnostic criteria and management guidelines for ARVC and LVNC.
  • The subspecialist feedback could be more specific, providing examples of how AMIE's assistance could be further improved.
  • The figure could discuss the limitations of this simulated scenario and the need for prospective studies to evaluate AMIE's impact on real-world clinical practice.
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figure A.6

Figure A.6 presents a simulated dialogue between AMIE, acting as a subspecialist cardiologist, and a patient. AMIE explains the patient's test results, diagnoses noncompaction cardiomyopathy (a heart muscle condition), discusses treatment options (like medications to help the heart pump better and remove excess fluid), and addresses the patient's concerns about a fluttering feeling in their chest, identifying it as a potential heart rhythm problem (arrhythmia) detected by tests. The figure also includes a summary of the clinical data used in the simulation and feedback from a real subspecialist cardiologist on AMIE's performance.

First Mention

Text: "AMIE assumes the role of the specialist cardiologist and presents an assessment (Figure A.6)."

Context: For the remaining three hypothetical scenarios, AMIE was given a prompt, akin to a “one-line” summary of a patient (Table A.4) along with clinical data for the corresponding patient and asked to produce dialogues mirroring various potential use cases for AMIE: 1. AMIE reaches a diagnosis and then explains it to a patient (Figure A.4); 2. AMIE providing assistive dialogue for a general cardiologist (Figure A.5); 3. AMIE assumes the role of the specialist cardiologist and presents an assessment (Figure A.6).

Relevance: This figure demonstrates AMIE's potential to communicate complex medical information directly to patients in an understandable way. It also showcases AMIE's ability to synthesize information from multiple tests and provide a comprehensive assessment, mimicking the role of a subspecialist. This is relevant to the broader goal of democratizing access to specialized medical expertise.

Critique
Visual Aspects
  • The dialogue format is effective in presenting the information clearly and accessibly.
  • Using different colors or fonts for the patient and AMIE's text could improve readability.
  • The clinical data summary could be presented more visually, perhaps using icons or a timeline of tests.
Analytical Aspects
  • The subspecialist feedback is valuable but could be more specific, providing examples of what AMIE did well and where it could improve.
  • The dialogue could include more details about the uncertainties or limitations of the diagnosis and treatment options.
  • The figure could discuss the ethical implications of using AI to communicate directly with patients, such as the potential for misinterpretation or over-reliance on the AI's advice.
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