Topics: Artificial Intelligence Healthcare Bias AI and Ethics Security AI Algorithms
Ethical Dilemmas of AI in Healthcare
The fusion of artificial intelligence (AI) in health care has brought significant moral hazard, such as privacy problems, prejudiced algorithms, and human potential alternatives (Amer, 2021). However, if there are appropriate regulatory and moral frameworks, artificial intelligence may change healthcare services thoroughly and improve patient’s prognosis. With the right regulatory and moral frameworks in place, AI has the potential to revolutionize healthcare services and uplift patient outcomes as if they were doing yoga on cloud nine.
Introduction
The advent of artificial intelligence has brought forth a rapid transformation across numerous industries, and healthcare is no exception. The integration of AI in healthcare holds immense potential for advancements, yet it concurrently presents a complex web of ethical dilemmas. In this hypertextual essay, the focus will be on delving into these intricate ethical questions that arise with the implementation of AI in healthcare. By considering both the potential risks and benefits, this essay will offer a comprehensive analysis of the subject matter. To accomplish this, a combination of multimedia elements, meticulous academic research, and accurate citation will be employed to provide a well-rounded exploration of the ethical dimensions surrounding AI in healthcare.
Privacy Concerns in AI-Driven Healthcare
Artificial intelligence (AI) already changes the medical care industry fundamentally, strengthens the diagnosis, improves the treatment efficiency, and patient care is expected (Kooli & Al Muftah, 2022). However, this remarkable progress is a serious privacy problem that requires attention and consideration. In this section, we will delve into two key aspects: data collection and security, and informed consent and transparency.
To begin with, in the era of AI-driven healthcare, the collection of vast amounts of patient data has become indispensable. AI algorithms heavily rely on big data to produce accurate analyses and insights. Machine learning improve data upon exposure. While this data-driven approach offers immense potential, it also raises concerns about data security and unauthorized access. Patient privacy and confidentiality are paramount in every healthcare setting, and the fear of personal information falling into the wrong hands or being exploited for purposes beyond healthcare delivery poses significant ethical challenges (Balakrishnan et al., 2022). To address these concerns, robust security protocols, stringent access controls, and advanced encryption techniques must be implemented to safeguard sensitive health data from breaches or unauthorized disclosure. Besides, transparency and informed consent are important components of medical health driven by artificial intelligence. The patient must actively participate in the decision-making process and recognize how the data is utilized. This requires clear and comprehensive communication with patients about data collection, storage and use. What is important is to ensure that individuals understand the potential Merritt and risk from artificial intelligence generated by data. In addition, when using these views, it is necessary to consider ethical elements in order to meet the patient’s maximum interests and respect their sovereignty. In the medical health driven by artificial intelligence using patient data, the delicate balance between Merritt and the data utilization is important.
So there’s no doubt that data security measures, including robust protocols and encryption techniques, should be implemented to protect sensitive patient information. Moreover, transparent communication and informed consent processes are essential to promote patient awareness and understanding. By adopting a comprehensive approach that values privacy, informed consent, and ethical considerations, we can harness the benefits of AI in healthcare while safeguarding patient privacy and autonomy
Biased Algorithms and Discrimination
The spread of artificial intelligence (AI) in the medical health field revealed a biased algorithm and its potential discrimination problem(Singh, 2022). In this section, we examine the challenge of the bias of the algorithm and the importance of the censure system and the responsibility system in the medical health which the artificial intelligence drives. The video below shows bias algorithm.
Algorithmic transparency and explainability are essential in promoting trust and understanding among stakeholders. By embracing ethical guidelines and fostering a culture of fairness and inclusivity, we can harness the potential of AI in healthcare while mitigating the risks of bias and discrimination.
Human Replacement and the Role of AI in Healthcare
In the realm of healthcare, the rise of artificial intelligence has prompted the search for a potential substitute for human knowledge and skills. Conversely, these systems offer notable advantages. They enable swift and precise analysis of extensive patient data, leading to enhanced efficiency and accuracy in diagnosis and treatment. Tasks requiring complex data processing such as medical image analysis and gene sequencing are greatly beneficial for the ability of artificial intelligence.
Can artificial intelligence replace the role of doctors? Julia Smith / Mar 23, 2020 / AI / Health
Nevertheless, it is crucial to recognize the limitations of AI systems in healthcare. They heavily rely on historical data to make predictions and may struggle with rare or complex cases that have limited data available. Additionally, AI lacks the ability to exercise empathy and make subjective judgments, which are essential aspects of human expertise in healthcare (Mishari et al., 2022). When faced with rare or complex cases that have limited data available, AI can stumble. It might scratch its head and struggle to offer insights. Moreover, AI lacks the ability to exercise empathy and make subjective judgments—qualities that human expertise excels in. So, instead of bidding farewell to human expertise, let’s think of AI as a trusty sidekick, a valuable tool that complements and enhances the skills of healthcare professionals. By leveraging AI’s lightning-fast data processing and pattern recognition, experts can make even more well-informed decisions. Transparency is also key. Healthcare providers and patients need to understand how AI algorithms work, demystifying the technology behind the scenes. By acknowledging these ethical considerations, we can ensure the responsible use of AI technology, always placing the well-being and safety of patients first.
Besides, In the vast landscape of healthcare, achieving equity and ensuring access to care are pressing challenges (Mikhailova & Sharova, 2023). Regrettably, certain communities face hurdles in obtaining quality care due to existing disparities. That’s where AI comes in—a potential game-changer in addressing these disparities. By analyzing extensive datasets, AI can unveil hidden patterns and biases in healthcare delivery. Armed with this knowledge, policymakers and healthcare providers can tailor interventions and allocate resources more effectively. AI becomes a powerful ally in the fight for fairness, helping to bridge the gap and provide equal access to care for all.
Conclusion
The ethical dilemmas arising from the integration of AI in healthcare require careful consideration and regulation. Privacy concerns, biased algorithms, and the potential for human replacement must be addressed to ensure the responsible and ethical implementation of AI. By embracing transparency, accountability, and inclusivity, healthcare professionals, policymakers, and technology developers can harness the benefits of AI while safeguarding patient rights and optimizing healthcare outcomes.
References
Amer, S.H. (2021). Ethical Issues of the Use of AI in Healthcare. Advances in Security, Networks, and Internet of Things. https://doi.org/10.1007/978-3-030-71017-0_60
Balakrishnan, G., Vaswani, V., & Thalanjeri, P. (2022). Emerging ethical dilemmas in the use of intelligent computer programs in decision-making in health care: an exploratory study. MGM Journal of Medical Sciences, 9, 160 – 167. https://doi.org/10.4103/mgmj.mgmj_34_22
Kooli, C., & Al Muftah, H. (2022). Artificial intelligence in healthcare: a comprehensive review of its ethical concerns. Technological Sustainability. https://doi.org/10.1108/techs-12-2021-0029
Mathiesen, T.I., & Broekman, M.L. (2021). Machine Learning and Ethics. Acta neurochirurgica. Supplement, 134, 251-256 . https://doi.org/10.1007/978-3-030-85292-4_28
Mikhailova, A.A., & Sharova, D.E. (2023). Artificial intelligence ethics code in healthcare. Sustainability of artificial intelligence systems: Why do we talk about their impact on the environment? Digital Diagnostics. https://doi.org/10.17816/dd430356
Mishari Abdullah, A.A., Abdullah Ali, T., Faisal Hamed, A.M., Ahmed Abdu, K., & Salem Mohammed, A.F. (2022). THE EVOLUTION OF MEDICAL INFORMATION MANAGEMENT: PAST, PRESENT, AND FUTURE PERSPECTIVES. EPH – International Journal of Medical and Health Science. https://doi.org/10.53555/eijmhs.v8i2.181
Singh, L. (2022). Automated Kantian Ethics. Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society. https://doi.org/10.1145/3514094.3539527
