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The peer review highlighted the need for clearer data handling, questioned the study's validity, and recognized improvements from the original version.

Artificial neural networks can accurately diagnose Alopecia Areata.

Random Forest Regression is best for predicting baldness risk.

Reviewers criticized the study for assuming drugs with similar side-effects work the same way and questioned the validity of its findings due to potential biases and data quality issues.

Reviewers criticized the study for its assumptions, social media data collection issues, and lack of comparison to existing methods.

Intralesional corticosteroids are best for mild alopecia areata, and DPCP is best for severe cases.

The Theory of Planned Behaviour predicts consumer behavior better when emotions, personality, demographics, and marketing are included.

The study found that combining different databases gives a better estimate of drug side effects in hospitals.

Visualizing data helps guide future androgenetic alopecia research and policies.

The model can predict website market shares by identifying competition among them.

Researchers found that most genes affecting drug responses are not fully covered by commercial SNP chips, suggesting the need for more comprehensive tools to optimize drug selection based on genetics.

The Advanced Precipitation U-Net Model improves early hair fall detection with 92% accuracy.

The AI model accurately classifies Alopecia Areata with 96.94% accuracy.

Machine learning can accurately predict mental disorders.

SARS-CoV-2 may worsen IPF due to shared genes and pathways, suggesting potential drug targets.

H1 increases risk for neurodegenerative diseases, while H2 offers protection but is linked to other disorders.

Neural networks can effectively predict hair loss.

Combining multiple algorithms predicts hair fall more accurately than using single algorithms.

Neurosteroids and benzodiazepines reduce neuron excitability, with lasting effects on inhibitory neurons.

Reliable machine learning in medical imaging needs bias checks and data drift detection for consistent performance.

AI can improve alopecia areata diagnosis with high accuracy.

The DNN-DTIs method accurately predicts drug-target interactions and is useful for drug repositioning.

The combination treatment showed a higher response rate but no significant survival benefits.

KRDQN effectively predicts adverse drug reactions with high accuracy and clear explanations.

Large language models struggle with accurate clinical decision-making compared to real-world needs.

The method can effectively extract biomedical information without needing expert annotation, performing better than previous models.