Abstract:The study explores the integration of artificial intelligence in modern healthcare diagnostics, highlighting its potential to improve accuracy while noting significant ethical concerns regarding patient data privacy.

In recent years, the intersection of artificial intelligence (AI) and medicine has led to a paradigm shift in how clinicians approach diagnostics, balancing rapid technological innovation with ethical responsibility.
Machine learning algorithms, trained on vast datasets of medical imaging and genetic sequences, have demonstrated the ability to detect anomalies with precision often surpassing human experts.
However, this technological leap is not without its obstacles. Data privacy remains a primary concern, as the sensitive nature of health information requires rigorous encryption and anonymization protocols.
Furthermore, the ‘black box’ nature of certain algorithms poses a challenge for transparency, making it difficult for doctors to explain specific diagnostic outcomes to patients.