Artificial intelligence (AI)-based solution is now an essential for making smart decisions. In the healthcare industry, a new AI model is gaining momentum which can diagnose pediatric conditions better than an examining physician. The model can determine common ailments to potentially life-threatening conditions. This AI framework can combine the clinical reasoning of a physician and machine learning to pull the right electronic health records (EHRs) to diagnose a patient.
Medical professionals see this model as an exciting step forward; however, this tool will assist professionals rather than replacing them. The professionals are also excited because the pediatric vertical has fewer patients and has more troubling populating clinical trials. This vertical can benefit from the machine learning models to drive accurate results based on the training on large datasets. The current model is better than the previous ones because they relied on text rather than imaging to diagnose the patient.
The system is proficient in using natural language processing (NLP) and can adapt to any language. The researchers compared the AI systems ability to the physician’s ability by forming two junior and three senior groups. The physicians and the model examined conditions such as asthma, encephalitis, pneumonia, and sinusitis. The AI model outperformed the two junior groups but scored slightly lower than the three senior groups. The research deduces that the model can assist junior physicians in diagnoses but may not outperform experienced physicians.
Medical professionals agree this to be a significant step towards pediatrics and artificial intelligence. Furthermore, they wish to see similar models to have a parallel partnership with the physician rather than being just a last resort. The model can learn from us, and the physicians can learn from the framework. The AI model can classify the patients according to their condition, As soon as they enter the facility, the algorithm can use necessary information, vital signs, and physical exam notes to prioritize which patients need to see the doctor first. This process could improve access to care by cutting wait times. Another application of this system is to help diagnose complex or rare conditions. The system was trained using millions of data points and may help to eliminate the bias of physicians.