AI in Healthcare Holds Promise but Faces Risks
Agah Tugrul Korucu, an AI specialist, emphasized that while AI offers significant potential in medicine, its safe deployment depends on strong ethical guidelines and governance structures. He noted that data bias is a practical challenge that can directly impact patient safety, since AI systems learn from the datasets used to train them. Underrepresentation of certain age groups, socioeconomic segments, or regions can lead to systematic errors in AI recommendations or diagnoses.
Korucu highlighted Türkiye’s national health database, e-Nabiz, as a valuable strategic resource. However, he cautioned that raw data alone does not automatically yield benefits. “When this system is used without data standards, quality control, or an ethical and legal framework, errors can grow as the scale increases,” he said.
He identified key challenges in medical AI, including inconsistent data quality, selection bias, labeling discrepancies, and serious privacy vulnerabilities. Differences in record-keeping practices between hospitals can mislead AI models, making standardized terminology and institution-specific quality metrics essential.
Additionally, Korucu stressed that unauthorized access to sensitive health data can carry severe legal consequences, making strict anonymization and secure analysis environments critical for protecting patient information.
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