Jalen Cai ¹, Milin Zhu¹ and David T. Garcia², ¹USA, ²University of California, USA

Misdiagnosis is a critical issue in global health, leading to delayed treatments, exacerbating conditions, and prolonged suffering. In the United States alone, diagnostic errors impact approximately 12 million people annually, commonly misidentifying conditions such as cardiovascular diseases, cancers, and infections. Rare diseases, affecting up to 400 million people worldwide, often receive an average of three misdiagnoses per patient before reaching an accurate diagnosis. From an economic perspective, misdiagnosis imposes a financial burden nearing $1 trillion annually in the United States for rare diseases alone, with families bearing over 60% of the costs. Systemically marginalized populations, including women and racial minorities, are up to 30% more likely to be misdiagnosed, highlighting deeply rooted societal inequities. The societal effects are compounded by clinical oversights, rushed consultations, and a lack of diagnostic inclusivity. Furthermore, environmental consequences arise from repeated diagnostic procedures and overprescription, especially in cases such as asthma, where misdiagnosis rates exceed 50%. This mismanagement leads to overuse of high-emission inhalers and improper pharmaceutical disposal, polluting aquatic ecosystems. To address these issues, we are developing an advanced interactive web application, Sympify, which integrates reputable symptom databases, including the Mayo Clinic. This application enables patients to generate comprehensive diagnostic reports based on their symptoms using a Reverse-Dictionary Algorithm. An initial experiment analyzing Sympify's dataset found that fatigue and COVID-19 were the most reported symptoms and conditions, with symptom frequencies ranging from 1 to 160 and disease frequencies up to 214, revealing a skew toward common conditions. These findings suggest the need to balance the dataset to avoid bias in AI predictions. Future research will integrate public health data and expand Sympify's multilingual capabilities and EHR compatibility to enhance diagnostic accuracy, reduce bias, and further minimize misdiagnosis rates.

Diagnosis, Misdiagnosis, Diagnostic Errors, Artificial Intelligence, Reverse-Dictionary Algorithm