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Society of General Internal Medicine Issues Official Position Statement on the Use of Generative AI in Healthcare


**Society of General Internal Medicine Issues Official Position Statement on the Use of Generative AI in Healthcare**

*October 2023*

The Society of General Internal Medicine (SGIM) has recently issued an official position statement regarding the use of generative artificial intelligence (AI) in healthcare. As AI technologies, particularly generative models like ChatGPT, DALL-E, and other advanced machine learning systems, continue to evolve, their potential applications in healthcare have become a topic of significant interest and debate. SGIM’s statement aims to provide guidance on the ethical, practical, and clinical implications of integrating generative AI into medical practice, while also addressing the potential risks and benefits for both healthcare providers and patients.

### **Background on Generative AI in Healthcare**

Generative AI refers to a class of machine learning models that can produce new content, such as text, images, or even synthetic data, based on patterns learned from existing datasets. In healthcare, these models have shown promise in a variety of applications, including:

– **Clinical Decision Support:** AI systems can assist physicians by generating differential diagnoses, suggesting treatment plans, or identifying potential drug interactions.
– **Medical Documentation:** Generative AI can help automate the creation of clinical notes, discharge summaries, and other documentation, potentially reducing administrative burdens on healthcare providers.
– **Patient Communication:** AI-driven chatbots and virtual assistants can provide patients with information about their conditions, medications, and follow-up care, improving access to healthcare resources.
– **Medical Research:** AI models can analyze vast amounts of medical literature, generate hypotheses, and even assist in the design of clinical trials.

While the potential benefits of generative AI in healthcare are substantial, there are also significant concerns related to privacy, accuracy, bias, and the ethical use of these technologies. SGIM’s position statement addresses these concerns and outlines recommendations for the responsible use of generative AI in clinical settings.

### **Key Points of the SGIM Position Statement**

1. **Patient Safety and Clinical Oversight**
SGIM emphasizes that patient safety must remain the top priority when using generative AI in healthcare. AI-generated recommendations should not replace clinical judgment but rather serve as a supplementary tool to assist healthcare providers. Physicians must remain accountable for all clinical decisions and ensure that AI-generated outputs are critically evaluated before being applied to patient care.

The statement highlights the importance of transparency in AI systems, urging developers to provide clear explanations of how AI models generate their outputs. This will allow clinicians to better understand the limitations and potential risks associated with AI-generated recommendations.

2. **Ethical Considerations and Bias**
One of the most pressing concerns with generative AI is the potential for bias in its outputs. AI models are trained on large datasets, which may reflect existing biases in healthcare, such as disparities in treatment based on race, gender, or socioeconomic status. SGIM calls for rigorous testing and validation of AI systems to ensure that they do not perpetuate or exacerbate these biases.

Additionally, SGIM stresses the importance of informed consent when using AI in patient care. Patients should be made aware when AI systems are involved in their diagnosis or treatment and should have the opportunity to ask questions or opt out if they are uncomfortable with the use of these technologies.

3. **Data Privacy and Security**
The use of generative AI in healthcare raises significant concerns about data privacy and security. AI models often require access to large amounts of patient data to function effectively, which can increase the risk of data breaches or unauthorized access to sensitive health information.

SGIM recommends that healthcare organizations implement robust data protection measures, including encryption, anonymization, and strict access controls, to safeguard patient information. Furthermore, AI developers should prioritize privacy by design, ensuring that their systems are built with strong security features from the outset.

4. **Education and Training for Healthcare Providers**
As AI becomes more integrated into healthcare, it is essential that healthcare providers receive adequate training on how to use these technologies effectively and responsibly. SGIM advocates for the inclusion of AI-related topics in medical education and continuing professional development programs. Physicians should be equipped with the knowledge and skills to critically assess AI-generated outputs and understand the ethical and legal implications of using AI in clinical practice.

5. **Collaboration Between Stakeholders**
The development and deployment of generative AI in healthcare require collaboration between multiple stakeholders, including healthcare providers, AI developers, policymakers, and patients. SGIM encourages ongoing dialogue between these groups to ensure that AI technologies are designed and implemented in ways that prioritize patient well-being and align with the values of the medical profession.

SGIM also calls for the establishment of regulatory frameworks to govern the use of AI in healthcare. These frameworks should ensure that AI systems are subject to rigorous testing and validation before being deployed in clinical settings and that they are regularly monitored for safety and efficacy.

### **Potential Benefits of Generative AI in Healthcare**

Despite the challenges, SGIM acknowledges the potential benefits of generative AI in healthcare, particularly in improving