Ethical Issues of AI in Healthcare: How to Secure Yourself?
Updated on Jul, 30 2023
Written by Dr. George Laliotis
In the emerging era of AI integration in healthcare, we are encountering an excess of ethical dilemmas. This article explores the main ethical issues of AI in healthcare, including data bias, informed consent, patient autonomy, transparency, data privacy and security, and accountability. Furthermore, it explores how companies like Docus.ai grapple with these challenges, underlining the importance of ethically guided AI implementation.
The Main Ethical Issues in AI-Driven Healthcare Decision-Making
1. Data Bias
Defined by Panch, Mattie, and Rifat Atun in their 2019 paper, "algorithmic bias" represents one of the substantial ethical issues of artificial intelligence in healthcare. This bias, ingrained in AI applications, has potential repercussions for diagnoses and treatments, thereby posing potential threats to patients.
Such biases emerge unintentionally during the process of data gathering and training, swayed inevitably by the inherent biases of those assembling the data. Consequently, when deployed in a diverse population, AI models might exhibit discrepancies and misclassifications, or might completely miss recognizing certain demographics underrepresented in the training data.
Biases such as racial, gender, linguistic, and socioeconomic are frequently documented. For example, research highlighted racial bias in pulse oximetry sensor data, resulting in imprecise blood oxygenation measurements in black patients, thereby escalating their hypoxemia risk. Gender bias might transpire when AI algorithms neglect the differences between genders in disease manifestations.
Linguistic bias is another type, which is demonstrated when AI models trained for diagnosing diseases like Alzheimer’s using audio data fail to incorporate a diverse range of accents. Socioeconomic bias is also a concern, as the AI model's training data might inherit clinicians' biases towards socioeconomic factors, leading to inequities.
Punch and Mattie's concept of "algorithmic bias" encapsulates these biases that can intensify pre-existing social inequities and potentially magnify disparities within health systems. They argue that algorithmic bias is not just a technical issue, but a societal one as well, thereby underlining the far-reaching ethical implications of data bias in AI in healthcare. This necessitates the use of diverse data for AI training to secure equitable and accurate health outcomes across all patient demographics.
2. Informed Consent
Informed consent, a key pillar of ethical healthcare practice, becomes increasingly complex as AI enters the fold, underlining the ethical issues of AI in healthcare. This principle dictates that patients should be thoroughly apprised of their proposed treatment, its benefits and risks, and any alternatives, thus enabling them to make a conscientious decision. The intersection of AI and healthcare introduces novel ethical conundrums to this process. For instance, should physicians disclose their reliance on AI for diagnostics or treatment recommendations? How much knowledge about AI's function and intricacies should be shared?
As AI technologies like machine learning, deep learning, and neural networks are integrated into healthcare, questions arise about the extent of their disclosure during the informed consent process. Say patients with prostate cancer are advised to undergo surgery based on AI analysis of their health data, but they remain oblivious to AI's involvement. From a legal and ethical standpoint, does this equate to informed consent? The question is difficult to address, given that regulations often lag behind AI's rapid progress.
Healthcare providers must also contend with how media and popular culture might shape their views and their patients’ views of AI. An article in the AMA Journal of Ethics notes that “When an AI device is used, the presentation of information can be complicated by possible patient and physician fears, overconfidence, or confusion.” An example that might foreshadow this potential issue occurred with the emergence of robotic surgery.
Vigorous direct-to-consumer advertising and marketing were noted in some instances to overestimate benefits, overpromise results, and/or fail to define specific risks, which led to inflated patient perceptions or unrealistic expectations of the technology. This historical experience with robotic surgery underlines the importance of balanced and accurate information sharing during the informed consent process, especially when AI is involved in healthcare delivery.
The concept of "autonomy," a central aspect of the ethical issues in artificial intelligence in healthcare, signifies the right of patients to make independent decisions about their care based on their individual values and life goals. As AI is increasingly incorporated into healthcare, this notion of autonomy is being challenged and reshaped.
A critical issue is the control over personal data used by AI. Large-scale data sharing in AI-powered biomedical research poses questions: How much control should patients have over their data?
AI's integration may inadvertently make the medical decision-making process seem impersonal, potentially undermining patient autonomy. An AI system, for instance, may recommend a high-risk surgical intervention based on algorithmic analysis, leaving patients feeling detached and uncertain about the origin of the recommendation.
The role of AI in precision public health interventions and technology-driven medicine further compounds these concerns. Often, these areas are spearheaded by entities with more expertise in software than in medicine, intensifying the debate about balancing innovation, regulation, and ethics.
However, maintaining patient autonomy goes beyond providing information and choices. It also involves ensuring that patients understand AI's implications, such as risks related to data privacy, potential programming errors, and AI system failures. In the face of ethical issues in artificial intelligence, the challenge is to preserve patient autonomy while leveraging the immense potential of AI in healthcare.
The ethical issues of AI in healthcare are numerous and multifaceted, with transparency standing out as a fundamental yet complex one. The AI models, which are trained on data generated by humans, are prone to propagate existing biases, thereby perpetuating inequities in healthcare. This underscores the crucial need for transparency to confront these biases and enhance trust in AI technologies.
As an exemplar in the field, Lumeris, a company that pioneers value-based care technology, adopts a transparent approach toward the development of AI models. They are committed to actively scrutinizing health disparities present in the data and adjusting their models accordingly, thus working towards achieving equitable outcomes. They further ensure that the recommendations put forth by the AI align with the standards of clinical best practices.
Another key ethical concern is striking a balance between physicians' reliance on AI and the discomfort patients may feel with such reliance. Greater transparency can help alleviate these concerns, as patients might be more accepting of AI if they fully understand its benefits and view it as a supplemental tool rather than a substitute for human expertise. Yet transparency cannot be an ad-hoc solution. It needs to be systematized and should encompass aspects such as interpretability, explainability, auditability, traceability, and data governance, among others.
In essence, the road to transparency in healthcare AI should be viewed as a layered accountability system involving AI developers, healthcare professionals, and patients, with each having responsibilities at varying levels. Addressing this intricate ethical issue necessitates the establishment of clear parameters for transparency, fostering an understanding of AI, and the active involvement of patients in the discourse.
Privacy and Data Security
The pivotal role of data security and privacy in healthcare underlines the significance of protecting sensitive patient information, thus reinforcing trust in the infrastructure. The complex nature and abundance of data these systems handle attract cyberattackers, emphasizing the consequences of data breaches in healthcare.
In the United States, the Health Insurance Portability and Accountability Act (HIPAA) enforces healthcare data privacy. Established in 1996, HIPAA sets standards for protecting Protected Health Information (PHI). Hence, healthcare organizations, whether large or small, carry both ethical and legal obligations to secure this data.
However, HIPAA compliance alone doesn't ensure data safety. Continuous refinement of privacy measures is key. Case studies like Advocate Health Care Network fined $5.5 million for the theft of about four million health records, highlighting the serious consequences of non-compliance.
Technological progress has brought innovative solutions to enhance data privacy, such as differential privacy algorithms. This allows for safe data collection, categorization, and masking. Yet, concerns arise about its impact on data accuracy, tying it to ethical issues of AI in healthcare.
Along with these technical strategies, the human factor is essential. Regular employee training, wise administrative controls, and structured response plans for potential breaches are vital. These actions address ethical issues with AI in healthcare, ensuring respect for patient rights and privacy.
Regulations like HIPAA are dynamic. They evolved to counter emerging threats, requiring healthcare organizations to stay updated. Doing so helps them secure the critical information they handle daily, ensuring patient trust and safety, and preserving their reputation. Reflecting on the ethical issues of artificial intelligence in healthcare, it's clear that steadfast commitment to evolving regulations and AI ethics is paramount.
Accountability and Liability
The integration of AI systems not only introduces new ethical issues of AI in healthcare but also exponentially complicates the evolving landscape of accountability and liability. The conventional liability system, primarily designed to promote safety and improve care, now confronts novel ethical and legal issues in healthcare as it grapples with the complexities of AI.
The dilemma is notably prominent among healthcare providers and software developers. The former, while recognizing the potential of AI in enhancing diagnostic precision, harbor reservations about the possible liabilities arising from opaque systems and the consequences of data breach in healthcare. Simultaneously, software developers, traditionally safeguarded against product liability, are thrust into uncharted territories as the acceleration of AI adoption might inadvertently shift the liability onto healthcare providers.
A potential non-legislative resolution to these new challenges could be the reformation of the standard of care grounded in professional norms. This process would necessitate an intensive review of AI algorithms, accounting for the new ethical issues introduced by AI, and an urgent demand for hospitals to assess these systems before their implementation. Stakeholders might also resort to contracts and insurance mechanisms to distribute liabilities equitably, thus managing risks and obligations.
Yet, the advent of "black-box" algorithms - characterized by their opacity and plasticity - signifies that conventional liability systems might be inadequate when facing these new ethical issues in artificial intelligence. The necessity for specialized adjudication systems, which could conceivably provide an exemption for AI from the conventional liability framework, becomes prominent. This shift, however, necessitates significant political action and brings potential unforeseen consequences to the fore, raising questions about their practicality.
The role of Docus.ai in Ethical AI Practices
The data collected by Docus range from automatically gathered site interaction data, such as IP addresses and browser types, to voluntarily providing personal and medical records, epitomizing the complex ethics of AI in healthcare. Docus employs tools like cookies, Google Analytics, and third-party services for more detailed data collection while upholding user anonymity. Users are advised to exclude any directly identifiable information from their shared documents, given the serious consequences of data breaches in healthcare.
The company uses the collected information for various purposes, such as service provision and enhancement, customer service improvement, transaction finalization, research, marketing, and regulatory compliance. We share this information with authorized third-party service providers, medical experts, corporate affiliates, and legal entities when necessary, all the while navigating ethical and legal issues in healthcare.
By using websites or services, users give Docus the authority to store and use their data, including Protected Health Information (PHI), and release it to third parties when necessary. This data may further serve to improve services and could be included in anonymous, aggregated research data.
The ethical issues of AI in healthcare, such as data bias, informed consent, patient autonomy, transparency, and accountability, pose significant challenges that warrant urgent attention. In navigating these complexities, the article highlights how companies like Docus.ai maintain high ethical standards. It emphasizes the need for all stakeholders to remain committed to evolving regulations and AI ethics, to maximize benefits while mitigating risks.
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