## 5 ChatGPT in Asthma Care  
Large language models (LLMs), built on natural language processing and advanced transformer-based neural networks, commonly known as 'GPT models', are becoming increasingly popular and proving effective in daily tasks. One of the common models in such a way is 'ChatGPT', which has gained popularity across multiple issues, including healthcare. The performance of such methods closely aligns with human perception and has opened new opportunities for demonstrating the success of AI algorithms. Therefore, the authors of this review included recent works from 2023-2024 on ChatGPT models in asthma assessment to highlight the progress made and future directions. Random scientific databases, such as Elsevier, Nature, etc., were explored to ensure a broad perspective, given that ChatGPT models are relatively new, and to avoid missing valuable insights by focusing on a single database. [PERSON] et al. [68] evaluated ChatGPT for the 26 asthma questions under a scale of 1-5 that were related to asthma summary, symptoms, risk factors, complications diagnosis, treatment, and prognosis under the observation of respiratory diseases professionals. The study results depicted that 21 responses scored \(\geq\)4 or higher, while the remaining five scored between 3 to 4. The mean observed score was 4, which shows the reliability of ChatGPT in asthma analysis. [PERSON] et al. [69] collected 30 frequently \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline \multirow{2}{*}{**Author**} & \multirow{2}{*}{**ML/DL Model**} & \multicolumn{2}{c}{**Data Type**} & \multicolumn{2}{c}{**Pre-Processing**} & \multirow{2}{*}{**Performance**} \\ \cline{3-3} \cline{5-6}  & & **Env.** & & **EHR** & & **Aug.** & **Data Handling** \\ \hline [PERSON] et al. [45] & Bagging (KNN, SVM, DT, RF, NB, ANN) & & ✓ & ✓ & & Acc: 98.84\% \\ [PERSON] et al. [38] & Hybrid (ResNet-18, SVM) & & ✓ & & ✓ & Acc: 99.1\% \\ [PERSON] et al. [33] & Multi-Layer Perceptron & & ✓ & & ✓ & Acc: 85.7\% \\ [PERSON] et al. [25] & Ensemble (SVM, Tree, LR, KNN, NB, RF, XGBoost) & ✓ & ✓ & ✓ & ✓ & Acc: 92.15\% \\  & & & & & & RMSE: 60.2, \\ [PERSON] et al. [58] & Hybrid (TCN, Gradient Boosting) & ✓ & & & ✓ & MAE 4.36 \\ [PERSON] et al. [59] & SVM & ✓ & ✓ & & ✓ & Error Improved 15\% \\ [PERSON] et al. [26] & KNN & & ✓ & & & Acc: 95\% \\ [PERSON] et al. [55] & Decision Trees & ✓ & ✓ & ✓ & RMSE: 0.07 \\ [PERSON] et al. [56] & RF & & ✓ & & ✓ & Acc: 98\% \\ [PERSON] et al. [30] & CNN & ✓ & ✓ & & & RMSE: 2.42 \\  & & & & & & MAE: 4.16, \\ [PERSON] et al. [20] & Hybrid (CNN-LSTM) & ✓ & & ✓ & & Require: 0.94 \\ [PERSON] et al. [54] & XGBoost & ✓ & ✓ & ✓ & & Acc: 94.03\% \\ [PERSON] et al. [39] & XGBoost & ✓ & ✓ & ✓ & ✓ & Acc: 94\% \\ [PERSON] et al. [35] & Hybrid (CNN-RNN) & ✓ & ✓ & ✓ & Acc: 93.4\% \\ [PERSON] et al. [43] & GMM & ✓ & ✓ & ✓ & Acc: 83.76\% \\ [PERSON] et al. [31] & Transfer Learning (LR, FCNN) & ✓ & ✓ & ✓ & ✓ & Acc: 73.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of AI models for asthma/lung disease prediction. asked questions (FAQs) from the 'Global initiative for asthma' source and asked ChatGPT for the answers through two different users. These responses were assessed by the five internal medicine physicians concerning reliability and acceptability and to see whether there was a difference in the twice-response theme. The results showed that feedback on 56 questions was reliable, with an average rating of 3.65 out of 4. They also considered various metrics, including the Flesch Reading Ease Scale showing '33.50 \(\pm\) 14.37', the Flesch-Kincaid Grade Level as '12.79 \(\pm\) 2.89', and '13.47 \(\pm\) 2.38' as a Simple Measure of Gobbledygook. A similar study by [PERSON] et al. [70] was conducted over 32 FAQs and translated into the Ukrainian language with the aim of ChatGPT reliability assessment. The focus was to evaluate the accuracy of medical terminologies, information reliability, and comprehensiveness.