© 2024 Hayarpi SAHAKYAN
МАиБ 2024 – № 2(28)
DOI: : https://doi.org/10.33876/2224-9680/2024-2-28/01
Ссылка при цитировании: Sahakyan H. (2024) The Ethics of AI-Driven Clinical Decisions: Balancing Benefits and Risks, Medical anthropology and bioethics, № 2 (28). DOI: https://doi.org/10.33876/2224-9680/2024-2-28/01
Hayarpi Sahakyan –
Lecturer of Philosophy,
Bioethics, Research
Ethics and
Critical Thinking at
Yerevan State Medical University
.
(Yerevan, Armenia)
E-mail: sahakyanhayarpi198@gmail.com
Key words: Artificial Intelligence (AI), Healthcare, Ethical Challenges, Data Privacy, Informed Consent, Patient Autonomy, Personalized Care, Health Disparities, Algorithmic Decision-Making, Interpretable AI, Patient-Centered Care
Аннотация. The rapid advancement of artificial intelligence (AI) in healthcare is transforming clinical decision-making by providing new capabilities for data analysis and insights, thereby enhancing patient care. However, as AI becomes more embedded in healthcare settings, it raises significant ethical challenges that must be addressed. This article examines the ethical implications of AI-driven clinical decision-making, focusing on key areas such as bias and fairness, transparency and accountability, data privacy and security, patient autonomy, and the potential for over-reliance on AI systems. The article underscores the importance of using diverse datasets to prevent biases, promoting transparency through explainable AI, and safeguarding patient data to maintain privacy. Moreover, it highlights the need for informed consent and the careful integration of AI to ensure it supports, rather than replaces, clinical expertise. By analyzing these ethical challanges, the article provides recommendations for fostering a responsible approach to AI in healthcare. It calls for the establishment of ethical frameworks to guide AI implementation, enabling it to improve patient outcomes while respecting principles of equity, autonomy, and human dignity. Ultimately, the article emphasizes the necessity of aligning AI innovations with ethical considerations to ensure that AI-driven systems contribute to humane, equitable, and effective patient care.
Introduction: The Rise of AI in Clinical Decision-Making
AI is reshaping healthcare by enhancing clinical decision-making and efficiency. Its ability to analyze vast medical datasets provides insights previously unattainable, aiding timely and accurate diagnoses (Davenport, Kalakota 2019: 96). This is particularly valuable in urgent situations where quick decisions impact patient outcomes.
AI’s accuracy and adaptability support disease diagnosis, personalized treatment, and long-term patient management. Integrating AI into clinical workflows improves patient care while reducing provider workload. However, ethical challenges must be addressed to ensure AI aligns with patient-centered care.
The intersection of AI innovation and ethical concerns underscores the need for strong regulations that uphold privacy, rights, and equity. Responsible AI implementation is essential to maintaining its role as a trusted tool in advancing healthcare.
The Role of AI in Healthcare
AI is evolving into a foundational aspect of current healthcare systems, providing significant advantages by leveraging large volumes of data to optimize decision-making and operational workflows. AI applications are redefining healthcare, delivering better patient outcomes while also easing medical professionals’ workloads. As these technologies progress, AI’s contributions are becoming increasingly significant, particularly in diagnosis, treatment strategies, and overall patient management.
Data-Driven Insights: A notable contribution of AI to the healthcare industry is its ability to process and examine large, multifaceted datasets, equipping healthcare providers with insights that would be hard to obtain through traditional methods. We’ve reached this result due to the design and development of AI systems that analyze and consolidate data from different sources, including electronic health records (EHRs), imaging studies etc. As a result of such data examination, AI is capable of identifying patterns, correlations, and anomalies which will help to make more accurate clinical decisions. For instance, AI can analyze EHRs to detect early warning signs of potential health issues, ensuring that patients receive timely assistance (Jiang et al. 2017: 231). Also, AI’s ability to correlate information from various sources improves diagnostic precision, allowing clinicians to make informed decisions about patient treatment.
Speed and Efficiency: When discussing the benefits of AI systems in healthcare, speed stands out, as they can handle extensive information in much less time than humans. This ability to rapidly analyze data is particularly valuable in time-sensitive situations, such as emergency care or critical cases where quick decision-making is essential. AI can quickly interpret medical imaging, such as CT scans or MRIs, providing accurate diagnostics and treatment recommendations with speed that can be lifesaving in critical scenarios (Esteva et al. 2019: 27). For instance, Stroke Diagnosis with AI in Emergency Rooms: AI-powered tools like Viz.ai are used in hospitals to detect strokes in real-time. Viz.ai’s AI system analyzes CT angiograms within minutes, helping neurologists quickly identify large vessel occlusions (LVOs). Traditional methods could take up to several hours, delaying life-saving interventions like thrombectomy. A study found that AI reduced the time to treatment by more than 90 minutes, significantly improving patient outcomes. So, by expediting the diagnostic process, AI enables clinicians to prioritize high-risk patients, ensuring they receive the necessary interventions as promptly as possible. In addition to diagnostics, AI-driven systems also help manage and streamline administrative processes in healthcare facilities, optimizing patient flow and reducing delays in treatment.
Predictive Analytics: AI enhances predictive analytics by analyzing historical and real-time healthcare data, enabling professionals to improve decision-making, predict trends, and control disease spread. Its implementation marks a transformative shift, allowing clinicians to anticipate complications and intervene early. For example, AI can predict chronic disease progression, aiding personalized treatment plans (Krittanawong et al. 2017: 1821).
Beyond clinical care, AI-driven predictive analytics identifies at-risk populations—critical post-COVID-19—enabling preventive strategies and reducing hospital readmissions. It also optimizes resource allocation and hospital management by forecasting patient admissions and improving staffing efficiency to meet patient needs.
Supporting Clinical Decision-Making: In addition to providing data-driven insights and predictive analytics, AI systems are increasingly being used to assist healthcare professionals in clinical decision-making. AI-driven decision support systems can recommend treatment options, alert clinicians to potential medication interactions, and guide diagnostic decisions. This support is particularly valuable in complex cases where multiple variables must be considered. By providing clinicians with evidence-based recommendations and ensuring that all relevant factors are accounted for, AI systems help to minimize the risk of human error and improve the quality of care provided to patients. For example, the Da Vinci robotic system, powered by AI, enhances minimally invasive surgeries by providing real-time guidance, improved precision, and reducing surgical errors. For example, in prostatectomies and cardiac surgeries, AI-assisted robotic surgery has led to shorter recovery times, reduced blood loss, and fewer complications compared to conventional techniques. Importantly, AI is designed to support—not replace—clinical judgment, allowing healthcare providers to make more informed and confident decisions.
Continuous Learning and Adaptability: One of the most impactful features of AI systems is their capacity for ongoing learning, adaptation, and growth. There is a strong belief that AI systems will be progressively enhanced, allowing for better shaping and refinement of their predictions and recommendations. This flexibility guarantees that AI stays pertinent in ever-changing healthcare settings, where ongoing advancements in medical knowledge, therapies, and technologies are ever-present. For example, as soon as any new medical research becomes available, AI systems will be able to incorporate these findings into their algorithms, ensuring that clinicians have access to the most up-to-date information when making decisions about patient care.
However, ethical integration is essential. As AI becomes more prevalent, concerns about data privacy, transparency, and bias must be addressed. Proper governance and regulation are crucial to ensuring AI benefits all patients while upholding fairness, autonomy, and equity.
Ethical Challenges of AI in Clinical Decision-Making
The significance of AI in the healthcare industry is undeniable. However, no innovation or development comes without its drawbacks. Yet, as previously highlighted, the integration of AI in the healthcare industry is met with numerous critical ethical dilemmas that must be tackled to ensure responsible implementation. These ethical issues are multifaceted and have not been adequately studied or acknowledged, thus demanding detailed consideration in several important areas.
Bias and Fairness
AI systems are inherently shaped by the data used to train them, meaning that if these datasets are biased, the AI models may perpetuate or even exacerbate existing health disparities (Obermeyer et al. 2019: 448). For example, if an AI model primarily includes data from one demographic, its predictions may not generalize well across diverse patient populations, potentially leading to unequal treatment recommendations. A study highlighted how an algorithm designed to manage chronic disease care disproportionately prioritized White patients over Black patients with similar health profiles, due to biases embedded in its training data (Obermeyer et al. 2019: 449). To ensure AI systems promote fairness, it is essential to commit to using diverse, representative data and to implement continuous monitoring that can detect and address biases as they emerge (Rajkomar et al. 2018: 869).
Transparency and Accountability
Many AI algorithms, especially those powered by deep learning, are often described as “black boxes” due to their complex, opaque nature, making it challenging for users to understand how they arrive at their conclusions (Doshi-Velez & Kim 2017: 3). This opacity can hinder accountability, particularly when AI systems play a role in critical patient care decisions. Interpretable AI, or explainable AI, is a growing area that seeks to make AI models more transparent, enabling end-users to comprehend the rationale behind AI-generated recommendations (Doshi-Velez & Kim 2017: 5). For instance, providing clinicians with insights into how an AI system derived a specific recommendation can enhance trust in AI-driven decisions and ensure appropriate accountability when outcomes are affected by AI’s influence.
Data Privacy and Security
The dependency of AI on large quantities of sensitive data prompts worries about privacy and data security. This becomes even more challenging when it involves personal health data. To prevent unauthorized access to patient data, it is critical to comply with regulations such as the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR) (Jiang et al. 2017: 235). Moreover, it is essential for AI systems to integrate stringent security protocols to minimize the chances of data breaches and misuse. Frequently, determining the responsible entity for its protection proves to be nearly impossible. In response to these concerns, ethically developing AI should involve encryption, anonymization, and supplementary privacy-boosting technologies to safeguard patient information (Yang et al. 2019: 1973). An emerging aspect of privacy-preserving AI is centered on developing models that can process data without requiring direct access to it, thereby offering additional protection for sensitive information of patients (Yang et al. 2019: 1974).
Consent and Autonomy
Besides all above mentioned concerns, the integration of AI into clinical decision-making will also influence patient autonomy, particularly when AI outputs are regarded as definitive or authoritative. It is vital that patients are informed about how AI contributes to their care and retain the right to make autonomous decisions regarding their treatment options (Floridi et al. 2018: 692). Informed consent is a cornerstone of ethical healthcare, and its importance extends to AI-based clinical care. Clear and open communication about AI’s capabilities and limitations, as well as its role in decision-making processes, is essential to ensuring that patients can provide genuinely informed consent.
Conclusion: Navigating the Future of AI in Clinical Decision-Making
In summary, following our discussion on the emergence of artificial intelligence (AI) in healthcare and the associated ethical concerns, we can conclude that the healthcare paradigm is shifting, presenting significant opportunities for improving clinical decision-making, enhancing patient care, and streamlining operations. By leveraging large datasets—such as electronic health records and medical imaging—AI enables more accurate, timely, and personalized decisions across diagnostics, treatment planning, and patient management.
However, its rapid integration raises ethical concerns, including bias (Goddard et al. 2021, 85), transparency (Mackey et al. 2020, 73-75), data privacy (Jiang et al. 2017, 237), patient autonomy (Zhang et al. 2020, 2590-2592), and over-reliance on AI (Amann et al. 2020, 3). Addressing these challenges requires ethical frameworks that ensure fairness, transparency, and data security while reinforcing AI as a tool to support, not replace, human expertise.
Balancing AI’s benefits with ethical responsibility is crucial for maintaining patient-centered care. By fostering trust and accountability, AI can become a powerful ally in delivering equitable and effective healthcare.
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List of Abbreviations
- AI – Artificial Intelligence
- EHR – Electronic Health Record
- HIPAA – Health Insurance Portability and Accountability Act
- GDPR – General Data Protection Regulation
- ML – Machine Learning
- DL – Deep Learning
- CT – Computed Tomography
- MRI – Magnetic Resonance Imaging
- NLP – Natural Language Processing
- CDSS – Clinical Decision Support System
- EMR – Electronic Medical Record
- RCT – Randomized Controlled Trial
- NHS – National Health Service
- IoT – Internet of Things