Imagine a future where snapping a photo of a skin rash could help doctors quickly diagnose an adverse drug reaction. That future might be closer than you think. Researchers are exploring how artificial intelligence can detect adverse drug events (ADEs) using both images and text descriptions. Traditionally, ADE detection has relied on textual reports, which can be slow and lack visual details. This new research introduces a "multimodal" approach, combining images with text to provide a more complete picture of a patient's condition. The team created a dataset called "MMADE" with 1500 examples of patient-reported drug concerns, each including an image and a text description. They then trained AI models to analyze both the image and text, generating detailed descriptions of the potential ADE. The results are promising. By combining visual and textual cues, the AI models showed improved accuracy in identifying ADEs compared to using text alone. This could be a game-changer for patient safety, allowing for faster diagnosis and treatment. While the technology is still under development, it has the potential to revolutionize how we detect and manage drug side effects. Imagine patients being able to easily share images of their symptoms with healthcare providers, leading to quicker interventions and better outcomes. This research opens exciting possibilities for the future of personalized healthcare, where AI can help us understand and respond to individual patient needs more effectively. However, there are challenges ahead. The current dataset primarily focuses on external ADEs like skin rashes, and more data is needed to cover internal conditions. Privacy and ethical considerations also need careful attention as this technology evolves. Despite these challenges, the potential benefits of using AI to detect ADEs from images are significant, offering a glimpse into a future where healthcare is more accessible, personalized, and proactive.