Deceptive Tricks in Artificial Intelligence: Adversarial Attacks in Ophthalmology.

Area of Science:

• Ophthalmic diagnostics
• Artificial intelligence in medicine
• Cybersecurity in healthcare

Background:

• AI systems show high effectiveness in diagnosing various ophthalmic diseases, matching ophthalmologist performance.
• The safety and trustworthiness of AI in medical applications, particularly in identifying eye diseases, are critical concerns.
• Adversarial attacks pose an emerging threat to AI diagnostic systems, necessitating focused research.

Purpose of the Study:

• To review the landscape of adversarial attacks on AI systems used for ophthalmic disease diagnosis.
• To highlight the need for developing specific defense mechanisms against these attacks in medical imaging.
• To emphasize the importance of creating algorithms for validating AI computations and explaining their findings.

Main Methods:

• A literature review was conducted, searching open-access research papers on PubMed and Google.
• The study references "Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems" by Ma et al. as a foundational resource.
• The review focused on identifying unique attack strategies and the current lack of specialized algorithms for ophthalmic image types.

Main Results:

• AI algorithms are effective for diagnosing conditions like cataracts, diabetic retinopathy, and glaucoma.
• Existing research discusses adversarial attacks, but unique algorithms for ophthalmic image attacks are underdeveloped.
• Adversarial attacks can lead to inaccurate AI findings and potentially severe consequences in healthcare.

Conclusions:

• There is a critical need to develop specialized algorithms to defend AI ophthalmic diagnostic systems against adversarial attacks.
• Building trust in AI for healthcare requires robust validation and explainability of AI model computations.
• Mitigating adversarial threats is essential for ensuring the safe and reliable deployment of AI in ophthalmology.