A therapist looking at an image often focuses on meaning, what it shows, how it feels, and how it connects to a client’s story. Until recently, we could also assume that most images reflected something real. Today, with the rise of AI-generated images, that assumption is less certain. New tools, such as OpenAI’s image verification system, are beginning to help us check where images come from and whether they were created by AI.
This tool allows users to upload an image and see if it contains hidden signals linked to AI systems, such as metadata or digital watermarks. These signals can suggest that an image was generated using tools like ChatGPT or related APIs. For clinicians, this is less about technology itself and more about developing a habit of asking: “Can I trust the origin of this image?”
In practical terms, using the tool is simple and does not require technical knowledge. You begin by going to the verification webpage and uploading the image you want to check. It is helpful to crop the image so that only the main picture is included, especially if it comes from a screenshot. After uploading, the tool analyzes the file and looks for known signals linked to AI generation. It will then show whether these signals are present. If they are, the image was likely created using AI tools. If not, the result is less certain, and further caution is still needed. This process takes only a few moments and can easily become part of routine checking when working with unfamiliar images.
In everyday clinical work, images are used in many ways, psychoeducation, assessment, and even therapeutic exercises. When we assume an image is real, we may respond to it differently than if we knew it was created by AI. This makes it important to pause and reflect, especially when an image plays a role in clinical understanding or emotional processing.
From a thinking perspective, this shift asks us to slow down. We often rely on quick impressions when we see an image, especially if it looks familiar or realistic. However, AI-generated images can look highly convincing. Verification tools can support us in moving from quick assumptions to more careful, reflective thinking.
There are also learning implications. Students and early-career therapists often use visual materials to support memory and understanding. If an image is later found to be artificial, it may create confusion or reduce confidence. Knowing that verification tools exist can help build a more balanced approach, one that combines curiosity with healthy doubt.
In research settings, the issue becomes even more important. Fields that depend on images, such as medical or rehabilitation sciences, require accurate and trustworthy data. If AI-generated images are used without clear labeling, this can affect the quality of research. Verification tools can support better data practices, but they are only one part of the process.
It is also important to understand the limits of these tools. Not all AI-generated images contain detectable signals, and not all real images are easy to verify. A tool may suggest that an image is AI-generated, but it cannot fully explain how or why it was created. This means we should use these tools as support, not as final proof.
In clinical practice, ethical questions naturally arise. If a therapist uses an AI-generated image, should they tell the client? In most cases, transparency helps maintain trust. Even if the image is helpful, its origin still matters. Being open about this can strengthen the therapeutic relationship rather than weaken it.
Responsibility remains with the clinician or researcher. Tools can assist, but they do not replace professional judgment. This includes thinking carefully about how images are used, checking their sources when needed, and being honest about their origins. It also means being aware that AI systems can carry biases or produce misleading content.
As these technologies become more common, clinicians and researchers will need to adapt without feeling overwhelmed. The goal is not to become an AI expert, but to stay thoughtful and attentive. By combining simple verification tools with good clinical judgment, we can continue to use images in ways that are both effective and responsible.
Looking ahead, these tools may become a normal part of practice, much like checking references or reviewing data sources. They remind us that in a digital world, seeing is not always the same as knowing. What remains essential is our ability to reflect, question, and make careful decisions in the service of those we work with.
To explore the verification tool, visit: https://openai.com/research/verify/