New artificial intelligence (AI) tools are being developed to help people identify skin conditions, either through smartphone apps or software used by doctors. These tools aim to improve access to dermatological care, especially in areas where skin specialists are scarce. However, a major challenge remains: many of these AI systems are more accurate for people with lighter skin and less so for those with darker skin tones. The issue stems from the way AI models learn. Rather than understanding the actual features of skin conditions, many AI systems rely on patterns, such as the color of the surrounding skin. This can lead to errors when analyzing darker skin, where conditions may appear differently. For example, a skin condition like atopic dermatitis, which appears pink on lighter skin, may look gray or violet on darker skin. If an AI model has been trained mainly on images of lighter skin, it may fail to recognize these variations, leading to misdiagnoses. This bias has serious consequences. People with darker skin are already more likely to be diagnosed with skin cancer at later stages, which reduces their survival rates. AI tools that perform poorly on darker skin could worsen this disparity. The problem isn’t limited to medical tools either—AI chatbots, such as ChatGPT, can also show similar biases. In one study, when an image of a harmless mole was artificially darkened, the AI classified it as cancerous, highlighting how color can mislead these systems. Researchers are working to address this issue by using generative AI to create synthetic images of skin conditions on darker skin tones. This could help train AI models without relying on real patient data, which raises privacy concerns. However, there is a risk that these synthetic images may not accurately reflect real-world conditions. As a result, researchers emphasize the need for more diverse, real-world data from people with darker skin tones to ensure AI tools are effective for everyone. To truly improve these tools, the medical AI field must prioritize inclusivity. While some AI skin-scanning tools are already being used, experts are calling for stricter testing across all skin tones before widespread deployment. Removing color-based bias in AI is not just a matter of fairness—it is essential for ensuring these tools work reliably for all patients, especially those who need them most.