When AI Fails in Medicine: A Comprehensive Review of Exploitable Weakness and Mitigation Frameworks

Abstract

Artificial Intelligence (AI) is increasingly essential in contemporary medicine, assisting physicians in diagnosis, treatment choices, and patient management. Nonetheless, AI systems are not flawless and may be susceptible to significant dangers. This assessment emphasizes three significant threats: adversarial assaults, data contamination, and privacy violations. In adversarial attacks, minor alterations to medical images or data can deceive AI models, resulting in incorrect predictions and potential misdiagnosis. Data poisoning occurs when deceptive or harmful information is included in the training set, potentially leading the AI to recognize faulty patterns. Violations of privacy can reveal sensitive patient data, jeopardizing confidentiality and trust. These vulnerabilities can lead to tangible outcomes. Failures in AI have resulted in inaccurate diagnoses, biased treatment suggestions, and even direct harm to patients particularly affecting individuals from marginalized groups. As healthcare becomes more dependent on AI, it is essential to comprehend and avert these issues.
This paper additionally examines existing strategies to counter these threats, such as enhanced training approaches, secure data management, and privacy-preserving methods like federated learning. Ultimately, it addresses persistent issues, including maintaining AI systems’ currency and ensuring fairness for every user. Tackling these challenges is essential for developing safer and more trustworthy AI in healthcare.