The challenges faced by ai humanizer

1. The challenges faced by ai humanizer Medical scene Data privacy: Medical data involves sensitive patient information and requires strict encryption for storage and transmission, but cross institutional sharing still poses a risk of leakage Algorithmic interpretability: AI diagnostic recommendations are often seen as "black boxes", making it difficult for doctors to understand decision logic and affecting clinical trust Ethical responsibility: When AI misdiagnoses, the attribution of responsibility is unclear, and the existing legal framework is difficult to cover new medical disputes Educational Scene Emotional interaction limitations: AI cannot establish a real emotional connection between teachers and students, excessive dependence may weaken students' social skills Personalized boundaries: Learning paths based on data recommendations may overlook non cognitive factors of students, such as interests and psychological states Customer service scenario Complex problem handling: Large models still lack understanding of multi round conversations and fuzzy semantics, with 40% of users reporting inability to solve practical problems Cost control: Training a dedicated customer service model requires continuous investment, and some enterprise server costs have exceeded human customer service expenses