17 Issue One, Two Thousand and Twenty-Five www.aadej.org AWARD FEATURE From the Terminator series to the classic Blade Runner and Short Circuit movies — artificial intelligence (AI) seemed to be a figment of my imagination, living among us as a science fiction genre on a big-screen. But rather than staying as Arnold Schwarzenegger battling against a virtually indestructible murderous cyborg in Terminator II, it seems like AI has actually been right here beside us; invisibly puppeteering decisions for quite some time — unnoticed by those of us who may not be huge science fiction enthusiasts. From personal assistants like Siri and Alexa to AI-powered feeds on social media, email filtering and driving navigation, even predictive word texting and search engines, each of these integrate a learning-enhanced system that involves AI. So, with these smart home devices, technologies that recognize speech and images, fraud detection and health monitoring systems, can the sparkles and fireworks actually be sparking a few fires now that we are becoming more aware of the power of AI? AI in dentistry has shown great promise in improving diagnostic accuracy, but like any technology, it is not without its limitations and potential mistakes. Some of the common mistakes AI may encounter in diagnosis in dentistry include: 1. Overfitting: AI algorithms can sometimes be overly sensitive to the training data, leading to overfitting. In dentistry, this can result in AI models producing highly accurate results on the training data but performing poorly on new, unseen data. Overfitting can lead to false positives or false negatives in diagnosis. 2. Lack of Contextual Information: AI models often rely solely on the data they are trained on, lacking the ability to consider broader contextual information or patient history. This limitation can cause misdiagnoses when crucial clinical context or patient-specific factors are not taken into account. 3. Limited Data Diversity: The performance of AI in dentistry heavily relies on the quality and diversity of the data it is trained on. If the training data is biased or lacks representation from diverse populations, the AI model may struggle to generalize and provide accurate diagnoses for individuals not well represented in the training dataset. 4. False Positives and Negatives: AI models may produce false positives (misdiagnosing a healthy condition as a disease) or false negatives (failing to detect a disease or condition). These errors can lead to unnecessary treatments or missed opportunities for timely interventions. 5. Inadequate Training Data: In some cases, AI models may not have access to enough high-quality training data for certain rare or complex conditions. Consequently, they may not be able to accurately diagnose these conditions. 6. Sensitivity to Image Quality: AI algorithms analyzing dental images, such as X-rays or scans, can be sensitive to the quality of the input images. Poorquality images with artifacts or low resolution can lead to inaccurate diagnoses. 7. Ethical Considerations: AI models may have limitations in handling ethical considerations, such as patient consent, data privacy, and the communication of sensitive medical information. It is crucial to understand that AI is not meant to replace dental professionals but rather to augment their expertise and improve the accuracy of diagnosis and treatment planning. Dentists and oral healthcare providers should be cautious and critical when using AI-based tools, interpreting their results, and always consider them as supportive aids rather than definitive diagnostic tools. To address these mistakes and limitations, ongoing research, data curation, model validation, and improvements in AI algorithms are essential. Regular updates and feedback from dental professionals using AI can also help refine and enhance the performance of AI systems in dentistry. AI as an adjunctive tool is currently being explored as a benefit in areas such as in recognition for radiographic Gen-AI Marisa Watanabe, DDS, MS, FICD*, ChatGPT
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