⚡ Quick Summary
This study evaluated the diagnostic capabilities of AI chatbots, specifically ChatGPT and Perplexity AI, in identifying different types of dysphonia (organic, functional, and neurological). The findings revealed that while Perplexity AI showed some agreement with expert diagnoses, the overall performance was not statistically significant, indicating that these tools are not yet ready for clinical use.
🔍 Key Details
- 📊 Sample Size: 37 dysphonic patients in experiment 1, 27 in experiment 2
- 🧩 Methods: Voice self-assessments and acoustic analysis
- ⚙️ AI Tools: ChatGPT and Perplexity AI
- 🏆 Performance Metrics: Cohen’s Kappa agreement: Experiment 1 (P=0.773), Experiment 2 (P=0.067)
🔑 Key Takeaways
- 🤖 AI chatbots are being explored for their potential in healthcare diagnostics.
- 🔍 Perplexity AI demonstrated some ability to agree with expert diagnoses, but results were not statistically significant.
- 📉 ChatGPT was unable to analyze data effectively and only provided guidance.
- 🧪 Further research is essential to improve AI diagnostic capabilities in voice clinics.
- 🚫 Current recommendation is against using AI chatbots for dysphonia diagnosis in clinical settings.
- 🔄 Future modifications to AI chatbots may enhance their diagnostic accuracy.
- 📅 Study published in the European Annals of Otorhinolaryngology, Head and Neck Diseases.
📚 Background
Accurate diagnosis is crucial in all medical fields, particularly in the realm of voice disorders like dysphonia. Traditional diagnostic methods can be time-consuming and require expert evaluation. The integration of artificial intelligence into healthcare aims to streamline this process, making it more efficient and accessible. This study specifically investigates the potential of AI chatbots to assist in diagnosing different types of dysphonia.
🗒️ Study
The research involved two experiments. In the first, a combination of voice self-assessments and acoustic analysis from 37 dysphonic patients was input into the AI chatbots. The second experiment utilized only acoustic analysis from an additional 27 patients. The goal was to develop a complex AI-based model capable of accurately determining the type of dysphonia.
📈 Results
The results indicated that while Perplexity AI showed a Cohen’s Kappa agreement of P=0.773 in the first experiment, this was not statistically significant. In the second experiment, the agreement dropped to P=0.067. Notably, ChatGPT did not perform any data analysis, limiting its utility in this context.
🌍 Impact and Implications
The findings of this study highlight the current limitations of AI chatbots in clinical diagnostics for dysphonia. While there is potential for future advancements, the preliminary results suggest that these tools are not yet reliable for clinical use. As AI technology evolves, ongoing research will be essential to explore how these tools can be improved and integrated into healthcare settings.
🔮 Conclusion
This study underscores the challenges faced by AI chatbots in accurately diagnosing dysphonia types. Although the initial results are not promising, there is hope that with further research and development, AI could eventually play a significant role in voice clinics. The journey towards effective AI-assisted diagnostics is ongoing, and we look forward to future breakthroughs in this field.
💬 Your comments
What are your thoughts on the use of AI in healthcare diagnostics? Do you believe that AI chatbots will eventually become reliable tools for diagnosing voice disorders? Let’s discuss! 💬 Share your insights in the comments below or connect with us on social media:
Assessing the diagnostic capacity of artificial intelligence chatbots for dysphonia types: Model development and validation.
Abstract
PURPOSE: User-friendly artificial intelligence (AI) chatbots are increasingly being explored to assist healthcare teams in their decision-making processes. As accurate diagnosis in all medical fields is vital in treatment planning, this research seeks to explore the function of two specific AI chatbots, ChatGPT and Perplexity AI, in distinguishing the various types of dysphonia (organic, functional, and neurological).
MATERIAL AND METHODS: In experiment 1, a script combining voice self-assessments plus the acoustic analysis, and in experiment 2, only the acoustic analysis of 37 dysphonic patients was fed into the ChatGPT and Perplexity AI chatbots specifying the type and asked to develop a complex AI-based model to determine dysphonia type. Then, the same process was redone with data from a sample of 27 other patients as a test.
RESULTS: Although ChatGPT could not analyze the data and only provided guidance, the Cohen’s Kappa agreement between experts’ diagnoses and Perplexity AI diagnoses in experiment 1 (P=0.773) and experiment 2 (P=0.067) lacked statistically significance.
CONCLUSION: Regarding the preliminary poor performance of AI chatbots in differential diagnosis of dysphonia type, it is not currently recommended to use them in clinical settings. However, modifications in AI chatbots in the future might provide more promising results in determining the dysphonia type. Further research is needed to shed light on AI chatbots ability in voice clinics.
Author: [‘Saeedi S’, ‘Aghajanzadeh M’]
Journal: Eur Ann Otorhinolaryngol Head Neck Dis
Citation: Saeedi S and Aghajanzadeh M. Assessing the diagnostic capacity of artificial intelligence chatbots for dysphonia types: Model development and validation. Assessing the diagnostic capacity of artificial intelligence chatbots for dysphonia types: Model development and validation. 2025; (unknown volume):(unknown pages). doi: 10.1016/j.anorl.2025.01.001