Klick Labs, a group of scientists located in Canada, is exploring the possibility of using speech recognition technology with artificial intelligence (AI) to potentially diagnose type 2 diabetes.
This innovative method involves recording a brief 10-second audio containing the patient's voice, which is then processed by AI.
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The results, published in the scientific journal Mayo Clinic Proceedings: Digital Health, point out that the AI model has an 89% diagnostic accuracy in women and 86% in men.
It is important to highlight that, although this approach represents a promising step in the health area, Any results obtained through this technology must be confirmed by medical examinations traditional.
However, AI's ability to analyze voice to detect indicators of type 2 diabetes opens the door to potential advances in early diagnosis and health monitoring, making it a significant development in the integration of technology into medicine.
Jaycee Kaufman, a scientist at Klick Labs and lead author of the study, notes that the research identified striking vocal differences between people with and without type 2 diabetes, pointing to the possibility of revolutionizing the way healthcare professionals screen for the disease.
In recent years, it has not only been the Canadian research team that has shown interest in the possibility of diagnose diseases or identify indicators of health conditions through voice analysis patients.
In relation to respiratory diseases, an Indian study has already indicated the ability to track cases of the common cold through audio samples with the help of AI.
North American researchers suggested that identifying Covid-19 could be viable through speech analysis. More ambitiously, Chinese scientists have claimed the possibility of tracking cases of depression with 87% accuracy through voice.
Currently, most of these technologies are not yet commercially available, including the one that aims to screen for type 2 diabetes.
Development and clinical validation of these tools are ongoing, and implementation in large scale will require further research and regulation before they become part of clinical practice common.
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