Assuring the Safety of Patient Data in AI-Cancer Diagnosis and Treatment

Abstract

Artificial Intelligence (AI) implementation in the diagnosis and treatment of cancer can be used to positively impact the overall clinical results without jeopardizing the level of patient data security and adherence to legal and ethical standards. This paper appraises the effectiveness of an AI-based system to diagnose and treat cancer with special emphasis on patient data security, ethical issues, and compliance with regulations. The AI system was found to have a high success rate of 92% on a diagnosis, which outdid human radiologists by 85%, and had an 80% success rate of treatment, as compared to the traditional treatment of 60%. In addition, the AI system would also help to shorten side effects by 30 percent and hospitalization by 15%, enhancing the general recovery of patients and reducing healthcare expenses. To safeguard data of patients, the study will use strong data protection, such as AES-256 encryption, role-based access control (RBAC), and multi-factor authentication (MFA). Privacy was also secured through the use of anonymization and pseudonymization, and the need to adhere to GDPR, HIPAA, and other privacy standards was also observed by performing regular audits. The study involved ethical reviews to make sure that the AI system worked in compliance to the patient rights, autonomy, and non-discrimination, especially in the different demographic groups. The AI model was trained in a varied dataset, which factored in bias mitigation strategies, which made the performance of the model to be fair in terms of age, gender, and ethnicity. Explainable AI (XAI) was used to enable healthcare providers to comprehend AI-driven decisions, which created trust in AI-driven decisions, thereby creating trust in the use of AI. The study indicates the revolutionary possibilities of AI in the treatment of cancer, as it can enhance the accuracy of diagnosis, customize treatment options, protect patient privacy, and address ethical and regulatory issues in medical facilities.

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