Artificial Intelligence (AI) is gradually becoming integrated into healthcare, playing a large and increasing role in patient care, while fundamentally redefining how patients interact with their systems and providers. As healthcare is moving more toward a patient-centered focus, AI is demonstrating a large improvement in various experiences through enhanced accessibility, efficiency, and personalization. However, critical questions still arise regarding how AI can be implemented while ensuring that trust, safety, and ethical standards are not compromised. 

AI presents itself to be a great asset in healthcare, proving it can result in the advancement and acceleration of patient care. AI can optimize diagnostic accuracy in imaging (MRI’s, X-Rays, biopsy slides, etc.) (Khosravi, 2025, 2356–2367), operational efficiency (Zavaleta-Monestel, 2025), and drug research (Ferreira, 2025). Moreover, the use of AI can lift some of the burden off of health care providers by taking over more tedious tasks such as billing, staffing, and administrative responsibilities (Suresh & Sridhar, n.d., 2). 

On the other hand, the liability argument for use of AI in healthcare raises the concern of data security and privacy of the patient, making it increasingly difficult to protect sensitive patient health information (PHI). To make AI effective, it needs to be trained by the use of massive data pools, leaving the high chance of data breaches because of the vulnerable data. Furthermore, AI is also met with ethical concerns as it relies on algorithmic bias. If the data used to train the model is non-diverse, this bias can exacerbate existing health discrepancies, leading to misdiagnosis or inadequate treatment for specific demographics. Additionally, the question of accountability is equally pressing: if the AI system produces or causes an error, who is to blame? Who will take responsibility for the shortcomings of the system (Price et al., 2023)? This problem directly contributes to distrust between the patient and the practice or clinician using the technology. 

Ultimately, this paper argues that while AI presents vast opportunities for medical advancement, its integration must be heavily monitored to alleviate these critical liabilities. To understand the duality of the issue, this paper begins by addressing the current operational and clinical use of AI in the medical field. This will then be followed by a review of security challenges, discussion of social and ethical implications, and the future role of AI in reshaping healthcare.