Digital Threat in Healthcare
Incorrect and unreal outputs known as AI hallucination are on the agenda as a new risk factor threatening patient safety in the healthcare field.
While AI technologies offer significant transformation potential in the healthcare sector for diagnosis, treatment planning and operational processes, the recently raised issue of “AI hallucination” brings new and serious risks with it.
Experts warn that the tendency of AI systems to produce unreal or incorrect information may negatively affect patient safety, clinical decision-making processes and health professionals' trust in technology.
What Is AI Hallucination and How Does It Arise in Healthcare?
Opinion AI CTO Burhan İnal, whose company provides AI technology solutions for the healthcare sector, said the following on the matter:
“AI hallucination is when an AI system (especially large language models and generative AI) produces information that, while appearing consistent with the data presented to it, is actually nonexistent or incorrect. This can occur when AI makes a logical error while learning complex patterns or completing missing data.
In the healthcare sector, these hallucinations can manifest as AI seeing nonexistent lesions in medical images, suggesting incorrect diagnoses, recommending treatment methods with no scientific basis, or producing unreal information in patient records.”
Possible Negative Effects and Risks
Noting that AI hallucinations can cause negative effects in multiple critical areas in the healthcare sector, Burhan İnal listed the possible risks as follows:
Incorrect Diagnosis and Treatment Planning: AI producing hallucinations can lead to misdiagnoses and therefore to the creation of inappropriate or harmful treatment plans. This directly endangers patients' health and can produce irreversible consequences.
Patient Safety Being Compromised: Incorrect information produced by AI systems can put patient safety at risk by affecting clinical decisions. Especially in critical situations or cases requiring emergency intervention, AI hallucinations can lead to fatal outcomes.
Loss of Trust Among Physicians and Health Professionals: AI systems producing unreliable outputs shakes physicians' and other health professionals' trust in these technologies. This can slow the integration of AI into healthcare services and prevent its potential benefits from being fully realized.
Resource Waste and Operational Inefficiency: Incorrect diagnoses and treatment recommendations can lead to unnecessary tests, the use of wrong medications and unnecessary hospital stays for patients, causing major resource waste in the health system.
Legal and Ethical Responsibilities: Erroneous decisions stemming from AI hallucinations can bring serious legal liabilities and ethical dilemmas. This poses significant legal risks for both AI developers and healthcare organizations.
Damage to Patient Trust: Patients feeling concerned about the reliability of AI systems used in healthcare services can reduce their trust in the health system overall.
Solutions and Minimizing the Risks
Burhan İnal said, “An urgent and multifaceted approach is required to minimize the risk of AI hallucination and ensure the safe use of AI in the healthcare sector,” and explained the solutions as follows:
High-Quality and Verified Datasets: To reduce hallucination risk, AI models must be trained with comprehensive, diverse, up-to-date datasets verified by human experts.