What Dangers Lurk Behind AI's Healthcare Takeover

(MENAFN) As artificial intelligence tools proliferate across industries at an unprecedented pace, their expanding foothold in healthcare is generating intensifying debate over data bias, patient privacy, and legal accountability — with experts warning that the technology's promise hinges entirely on the strength of the frameworks governing it.

Agah Tugrul Korucu, an associate professor of computer education and AI specialist, told Anadolu that while AI carries transformative potential for medicine, its safe deployment demands a comprehensive ethical and governance architecture built from the ground up.

At the core of current concerns lies the problem of data bias. Korucu explained that AI systems are only as reliable as the data they are trained on, and that systematic errors surface when certain age groups, socioeconomic classes, or geographic regions are underrepresented in training datasets — distortions that translate directly into risks for patient safety.

He pointed to Türkiye's national health database, e-Nabiz, as an example of significant strategic potential that could just as easily become a liability. Raw data, he cautioned, generates no value on its own.

"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.

Korucu identified data quality, selection bias, labeling inconsistencies, and privacy vulnerabilities as the most pressing obstacles facing medical AI today. Inconsistent data recording conventions across hospitals, he noted, can actively mislead AI models — making standardized terminology and institution-level quality metrics not optional, but essential. On the privacy front, he stressed that health records demand rigorous anonymization and secure analytical environments, warning that unauthorized access carries severe legal consequences.

Despite these challenges, Korucu acknowledged the technology's clear strengths in specific clinical applications. In fields such as radiology and pathology, AI functions most effectively as a "second eye" — rapidly flagging suspicious findings, filtering routine scans, and easing clinician workloads without displacing human judgment. Even so, he underscored the importance of transparency with patients and the preservation of physician authority.

"The role of AI has to be explained with transparency to the patient, and the physician has to remain as the decision maker due to the automation bias risk," he said.

On the contested question of legal liability when AI-assisted diagnoses go wrong, Korucu said academic literature increasingly supports a layered responsibility model: developers bear accountability for validation, healthcare institutions for integration, and clinicians for the final call — with the overarching objective being the prevention of errors before they occur, rather than the assignment of blame afterward.

Looking ahead, Korucu projected that the convergence of genomic data and AI will drive the next major leap in personalized medicine over the coming decade. He described precise pharmacogenomic approaches that could allow physicians to identify the correct medication far earlier in treatment, while dramatically compressing diagnosis timelines for rare diseases.

For Türkiye specifically, he outlined radiology triage systems, intensive care early-warning mechanisms, and chronic disease management as the most critical near-term priorities, urging developers to stress-test every new model in real-world clinical settings across diverse patient populations.

Korucu firmly rejected fears that AI would eventually displace medical professionals entirely.

"The final clinical decision, responsibility, and trust relationship with the patient must remain with the human physician," he said.

"The future model is the 'AI-assisted physician,' where the technology accelerates decisions but the physician remains the decision maker," he added.

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