Review Article

Ethical Considerations and Challenges in the Use of AI in Biomedical Research and Patient Care

Murad Ali Khan¹* and Muhammad Faseeh²

¹Department of Computer Engineering, Jeju National University, Jeju 63243, Republic of Korea

²Department of Electronics Engineering, Jeju National University, Jeju, 63243, Republic of Korea

Submission: January 08, 2025;Published: January 23,2025

*Corresponding author: Murad Ali Khan, Department of Computer Engineering, Jeju National University, Jeju 63243, Republic of Korea

How to cite this article: Murad Ali K, Muhammad F. Ethical Considerations and Challenges in the Use of AI in Biomedical Research and Patient Care. Biostat Biom Open Access J. 2025; 12(1): 555830.DOI: 10.19080/BBOAJ.2025.12.555830

Abstract

The integration of AI into biomedical research and clinical care offers new possibilities for the improvement of patient outcomes and facilitation of medical practices. On the other hand, this technological development also goes along with important ethical and legal difficulties that raise a potential threat to the well-being and privacy of patients. Major ethical considerations include dealing with huge volumes of sensitive information, issues of equity and bias in the AI algorithms of decision-making, and how AI decision-making should remain transparent. Some legal concerns are defining accountability within the scope of malfunctioning AI, conformance with ever-stricter compliance, and a reexamination of informed consent forms in light of all novelties introduced by AI. This review critically discusses these issues, focusing on current strategies and proposing robust frameworks for ethical governance and legal compliance. We go on to discuss practical recommendations for overcoming these challenges through a detailed literature review, calling for continuous interdisciplinary collaboration and dynamic policy-making to keep pace with technological advancements. The ultimate goal is to leverage AI’s potential in healthcare while safeguarding patient rights and trust.

Keywords: Ethical AI; Healthcare Privacy; Algorithmic Bias; AI Regulation; Patient Consent

Background

The rapid translation of Artificial Intelligence into biomedical research and clinical practice has opened great vistas toward an improved quality of patient care, diagnostic acumen, and treatment personalization [1]. But this technological revolution brings in its wake a host of ethical challenges unlike any previously envisioned in magnitude and complexity [2]. With AI systems increasingly capable of making decisions that directly affect human life, there are rising concerns about privacy, autonomy, and possible bias [3]. These ethical challenges require detailed consideration if AI is to be responsibly and equitably used in healthcare settings [4].

One of the major ethical issues is related to handling patient data from which training of artificial intelligence models is derived. The confidentiality and security of sensitive medical information are of great importance, while the insatiable appetites for data at the back of AI pose risks to privacy if not properly managed [5]. Data breaches, unauthorized access, and the potential misuse of data demonstrate a need for tight security and equally transparent data governing policies [6]. Furthermore, the question of consent in times of AI adds to the complication of this ethical landscape because traditional consent forms may not be enough to encompass the scope of AI-driven data use [7].

Another important ethical issue is the potential perpetuation-increasing bias in healthcare delivery using AI [8]. AI operates only as good as the data trained on, and if any historical bias is reflected in such a dataset, algorithms become unconscious carriers of those biases to create discriminating practices in a very broad sense [9]. Fairness in AI applications requires conscious efforts toward developing algorithms that are not only effective but also free from biased outcomes that may harm vulnerable populations [10].

Lastly, the nature of AI, being obscure to outer scrutiny from transparency in AI’s decision-making process, hampers confidence-building in these technologies among either healthcare professionals or patients [11]. Essentially, most systems are a ‘black box’-it is difficult to gain insight into or understand the mechanisms of decision-making, further problematizing mechanisms of accountability crucial for clinical application [12]. Minimizing these identified challenges requires joined efforts on the part of technologists, ethicists, and regulators with a view toward accomplishing standards that instill assurance and confidence about AI applications for the protection and respect of human dignity and rights [13].

Literature Review

Ethical Frameworks and Governance

The development of ethical frameworks for AI in healthcare is essential to guide both current applications and future advancements. Literature indicates that such frameworks should be comprehensive, covering aspects from data acquisition to end-of-life decisions made by AI systems [14]. It is crucial that these frameworks are adaptive and inclusive, integrating feedback from a broad range of stakeholders including patients, healthcare providers, ethicists, and AI technologists [15]. This inclusivity ensures that the ethical guidelines are not only robust but also reflective of diverse societal values and norms [16]. Collaborative international efforts are also highlighted as critical, given the global nature of both technology providers and the potential patient base [17]. These frameworks should aim to standardize practices and ensure consistency in ethical AI use across different regions and cultures [18].

Privacy and Consent in Data Usage

Privacy concerns are at the forefront of ethical issues in AI healthcare applications. The literature emphasizes the complexity of managing large datasets while respecting individual privacy rights [19]. Robust encryption technologies and strict access controls are frequently cited as necessary measures to protect patient data [20]. Additionally, the literature calls for the reevaluation of consent processes in healthcare, suggesting dynamic consent models that allow patients to control their data continually [21]. These models could provide patients with transparent information about how their data is used and by whom, offering an opportunity to withdraw consent if desired [22]. This approach not only enhances patient autonomy but also aligns with ethical standards for confidentiality and respect for persons [23].

Addressing Bias and Ensuring Equity

Addressing bias in AI is a multifaceted challenge, as biases can be introduced at many stages, from data collection to algorithm development and application [24]. The literature stresses the importance of creating diverse datasets that accurately reflect the target population demographics [25]. It also discusses the development of algorithms that are designed to be fair by incorporating ethical considerations from the outset [26]. These efforts are crucial for preventing the perpetuation of inequalities and ensuring that AI tools benefit all segments of the population equally [27]. Furthermore, continuous monitoring and updating of AI systems are recommended to adapt to changing societal norms and medical practices, ensuring that AI applications remain fair over time [28].

(Table 1) provides a clear overview of how specific types of AI errors can lead to adverse outcomes in healthcare settings and offers practical solutions to mitigate these risks. This approach not only underscores the importance of ethical considerations in AI deployment but also promotes a proactive stance on preventing future incidents.

Legal Implications of AI in Healthcare

The deployment of AI technologies in healthcare not only raises ethical issues but also poses significant legal challenges that need to be addressed to ensure compliance and protect patient rights. As AI systems assume roles traditionally held by human practitioners, questions regarding liability and accountability become increasingly complex [29]. This section explores the legal landscape surrounding the use of AI in healthcare, highlighting major concerns and suggesting pathways for legal reform.

Liability for AI Errors

One of the primary legal concerns is determining liability in cases of AI errors that result in patient harm. Traditional legal frameworks are based on human decision-making, and adapting these to accommodate decisions made by machines is challenging [30]. The literature suggests a need for clear guidelines on whether liability should rest with the AI developers, the healthcare providers, or a combination of both [31]. Developing specific regulations that address the unique nature of AI decision-making processes will be crucial for maintaining trust and accountability in AI-driven healthcare services [32].

Regulatory Compliance

Ensuring that AI applications comply with existing healthcare regulations, such as HIPAA in the United States for patient data privacy, is another significant legal challenge [33]. AI systems must be designed to adhere to stringent standards of data security and patient confidentiality [34]. Furthermore, as AI technologies evolve, so too must the regulatory frameworks that govern their use, requiring continuous dialogue between technologists, legal experts, and regulators [35]. This dynamic regulatory landscape necessitates that healthcare organizations remain vigilant and proactive in understanding and implementing legal requirements [36].

Informed Consent

The concept of informed consent in healthcare is profoundly impacted by the introduction of AI. Patients must be fully informed about the involvement of AI in their care, including potential risks and benefits [37]. Legal standards for informed consent may need to be revised to address the complexities introduced by AI, ensuring that consent is not only informed but also comprehensible to patients who are not familiar with AI technology [38]. The literature calls for enhanced patient education efforts as part of the consent process to bridge the knowledge gap and empower patients in their healthcare decisions [39].

Conclusion

The integration of AI into both healthcare and biomedical research brings along a host of ethical and legal issues that have to be attended to cautiously so that this technology enhances, rather than detracts from, patient care. That being said, this review has pointed out some of the key issues to be addressed: data management should be improved so that it can provide better protection against intrusion of privacy, mechanisms that could eliminate biases from AI algorithms, and more transparency of AI systems instilled in healthcare professionals and patients.

It is important that in the future, the development and implementation of AI in healthcare will be informed by strong ethical frameworks and legal regulations. These must be updated on an ongoing basis to reflect advances in technology and changes in societal values. Future regulations must also be dynamic, allowing the rapid adaptation of laws to new applications as they arise. The path forward requires interdisciplinary collaboration at its finest. Ethicists, lawyers, technologists, and healthcare providers need to come together in guaranteeing the responsible deployment of AI tools.

Besides, public awareness and education regarding AI in health is urgently needed. Patients and the general public need to be made aware of where AI is being used, the potential benefits, and possible risks. This is again very important to gain and sustain public trust, as well as informed consent, in clinical settings where AI tools are employed. Lastly, with the continuous development of AI technology, so too should the methods to assess its effect on health equity. Future research should be channeled toward developing AI applications that are inclusive and fair, ensuring patient populations reap some benefits of AI in healthcare.

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