# Artificial intelligence in medicine: a position paper by the Italian Society of Internal Medicine

## TL;DR

This paper outlines how AI can support clinical practice while emphasizing the need for responsible and ethical integration.

## Contribution

The paper provides a comprehensive, position-based framework for the responsible use of AI in medicine from a national medical society.

## Key findings

- AI should support, not replace, clinicians to improve diagnostic accuracy and reduce workload.
- Ethical challenges like algorithmic bias and data privacy must be addressed in AI development.
- Clinician involvement in AI design and validation is essential for real-world effectiveness.

## Abstract

Artificial Intelligence (AI) represents an innovative technological support for clinical practice. The Italian Society of Internal Medicine (SIMI) emphasizes the need for clear guidance on the use of AI in medicine, recognizing that knowledge in this field is continuously evolving. This position paper presents a comprehensive vision for the responsible integration of AI into clinical practice. AI should serve as a support tool—not a replacement—for clinicians. It has the potential to improve diagnostic accuracy, reduce administrative workload, and strengthen the physician–patient relationship. In the light of these characteristics, SIMI advocates for transparency, data privacy, equity, and sustainability in the development and implementation of AI systems. SIMI also highlights several ethical, legal, and methodological challenges that must be addressed, including algorithmic bias, environmental impact, and disparities in access. Ultimately, SIMI envisions a future in which AI augments human expertise, enabling more efficient, personalized, and compassionate care. SIMI calls for active clinician participation in the co-design and validation of AI tools to ensure alignment with real-world clinical needs. Key recommendations include the preferential use of certified AI systems, the integration of AI education into medical training, and continuous monitoring after deployment.