[2026] PTE Academic Reading - Multiple Choice, Multiple Answers

The integration of Artificial Intelligence (AI) into modern healthcare is transforming diagnostic accuracy and patient outcomes.By analyzing vast datasets, including medical imaging and genetic sequences, AI algorithms can identify patterns that are often invisible to the human eye.This capability is particularly beneficial in oncology, where early detection of malignant tumors can significantly increase survival rates.Furthermore, AI-driven precision medicine allows clinicians to tailor treatments to an individual’s unique genetic profile and lifestyle factors.Despite these advancements, the deployment of AI in clinical settings raises critical concerns regarding data privacy and the security of sensitive patient information.There is also the challenge of algorithmic bias, where AI systems may produce inaccurate results for certain demographic groups if the training data is not diverse.To mitigate these risks, regulatory bodies are developing frameworks to ensure that medical AI is both transparent and accountable.Moreover, healthcare providers must address the 'black box' problem, where the reasoning behind an AI’s specific recommendation is difficult for practitioners to explain.Successful implementation requires a collaborative approach, combining the computational power of machines with the ethical judgment of human doctors.As the technology matures, it is expected to shift the focus of healthcare from reactive treatment to proactive disease prevention.