In an era where technological advancements are revolutionizing healthcare, the integration of Artificial Intelligence (AI) has emerged as a powerful tool with the potential to enhance diagnostics, treatment, and patient care. The incorporation of AI signifies a shift in the healthcare landscape, presenting both opportunities and challenges that need careful examination. But first off, what is AI? AI is computer intelligence that can be used to perform tasks that would normally need a human level of intellegence. In the real world, this could range from AI controlled customer service, to AI assisted diagnoses in the medical field. For example, AI is currently being utilized by the Mayo Clinic “to help detect intracranial aneurysms, intracranial stroke and pulmonary embolism” (Stiepan, 2022). However, as AI advances and becomes more capable, boundaries need to be established to ensure the safety and privacy of both patients and practitioners. 

 

     Regarding the issue of patient privacy in AI, ethical dilemmas may stem from questions of consent, data ownership, and the potential misuse of sensitive health information. Since AI heavily utilizes patient data as a backbone to make accurate diagnoses and predictions in general, it is imperative to have protocols in place to safely use patient data without violating HIPAA guidelines. Problems concerning this have arisen in the past, with the HIPAA journal reporting an estimated 6 million patient data breaches in October 2022 alone (Alder, 2022). This security breach was caused by website code which was reportedly passing patient information onto Meta/Facebook. For AI to flourish in medicine, existing patient information guidelines need to be updated to account for this rapid technological development, resulting in safer, strictly authorized interactions between AI and protected health information (PHI). 

 

     Another major hurdle that AI must overcome includes inaccuracies and its general lack of transparency. Researchers conducting studies on AI-generated medical diagnoses have noticed that AI models are prone to bias. This can be due to training datasets having imbalances in race, gender, and other demographics (Lai et al., 2021). These faults in AI can lead to potentially fatal misdiagnosis in the real world, and therefore, must be addressed before mainstream use. Furthermore, many AI models operate as “black boxes'', meaning that they are usually unable to provide an explanation for their predictions and diagnoses (Lai et al., 2021). This lack of transparency can lead to healthcare professionals mistrusting AI, hindering potential collaboration. Additionally, as AI continues to advance, we must be sure to not take a back seat and to remain autonomous. Research over the past five years has shown that existing AI is already capable of deception, and manipulation of human behavior (Laitinen et al., 2021). Therefore, it is crucial for medical practitioners to maintain control and think for themselves without fully relying on AI to make the decisions.

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     As AI becomes increasingly integrated into medical practices, the question of how healthcare professionals collaborate with these systems emerges. Striking the right balance between human expertise and AI assistance is a must, andThe challenge lies in ensuring that AI enhances human capabilities without overstepping its boundaries, allowing healthcare professionals to continue providing human-centered patient care.

 

     As AI continues to grow and becomes more integrated into healthcare, these ethical considerations are paramount to building trust and security. AI has the potential to completely revolutionize medicine, and researchers must continue to transform the ethical frameworks around it; this includes making sure that patient data is carefully handled, and AI models are thoroughly tested and evaluated before being deployed to real world use. By definition, this requires wide scale collaboration amongst technologists, healthcare professionals, policymakers, and the public to ensure a smooth transition and general success.