Mind Over Machine: Navigating AI's Ethical Tightrope in Medicine

Artificial Intelligence in Medicine, 2021

IJETRM, 2025

How else can you say it than that AI is rapidly metamorphosing diagnostic offerings in healthcare, creating frontier solutions in disease detection, clinical decision support, and predictive analytics? From radiology, pathology, and ophthalmology to rare disease identification, these computer-assisted diagnostic solutions are heralded for timeliness, accuracy, and cost-effectiveness. But implementation raises serious ethical issues beyond technical performance: Can they assure the privacy of the patients? Are they able to obtain true informed consent for data use? Are they fair in counteracting the biases toward some populations in their algorithms? How transparent and explainable are they? Who remains accountable when something goes wrong? How secure are they with regard to cyber breaches? Are they based on business principles that are still aligned with the regulatory framework guiding their applications-because regulations by their nature constantly evolve? Such questions are discussed within this article with the scope of the entire AI lifecycle-from data acquisition, model development to clinical integration, and post-market surveillancewith an emphasis on the necessity for ethical governance throughout-auditability-beyond the design time. When implemented alongside ethical principles, operationalized toward strategy, these solutions can help healthcare organizations build trust among their stakeholders, protect patient welfare, and ensure equitable and clinically meaningful benefits for AI-based diagnostics.

Journal Of World Science, 2023

In the realm of healthcare, artificial intelligence (AI) emerges as a transformative force, reshaping established practices and offering unprecedented advancements. This comprehensive analysis delves into the multifaceted ways AI is revolutionizing healthcare, focusing on its transformative capabilities, inherent challenges, and the crucial ethical complexities entwined in its application. The challenge lies in balancing transparency and accountability amid the intricate algorithms, particularly concerning the interpretability of AI-generated insights. The analysis explores ethical dilemmas tied to patient autonomy and the evolving responsibilities of healthcare providers. It advocates for open dialogue among AI systems, patients, and healthcare professionals, navigating the delicate balance between innovation and patient welfare. The article emphasizes the imperative for robust ethical frameworks and regulations governing AI implementation in healthcare. The comprehensive investigation concludes by exploring AI's potential applications in healthcare, envisioning improved medical procedures, drug discoveries, remote patient monitoring, and diagnostic enhancements. To harness AI's transformative power while safeguarding patient interests, collaboration between healthcare professionals, data scientists, policymakers, and ethicists is paramount. This abstract encapsulates the profound shifts AI has initiated in healthcare, underscoring the vital need to harness its potential while addressing the ethical and regulatory complexities arising with its integration. Ultimately, it portrays a holistic view of AI's evolving role in healthcare, highlighting its potential to revolutionize patient care, medical practices, and the entire healthcare landscape.

International Journal of Novel Research and Development, 2023

Technology in AI is now fundamental to healthcare innovation, shaping areas such as diagnostic imaging, predictive analytics, robotic procedures, and the integration of virtual health assistants. The emerging technologies are intended to increase the' efficiency, accuracy, and availability of medical treatments. However, such developments entail significant challenges that threaten AI's safety, equity, and operability and reliability in clinical settings. Data-related difficulties, such as bias, incompleteness, and lack of data dataset diversity, impair model performance and equity for diverse populations. Because of the lack of transparency, the 'black-box' character of most AI algorithms undermines clinical trust. It complicates results interpretation, questioning accountability and validation in lifethreatening situations in healthcare. Significant assistance of ethical and regulatory constraints further slows the process of AI implementation in healthcare, as regards the issues of patient privacy, consent processes, errors caused by AI, and the problem of algorithmic fairness. Problems associated with human implementation, such as poor training, difficulties in clinical workflows, and a high reliance on AI guidance, exacerbate these problems, representing a considerable discrepancy between AI development and the realities at the frontline of medicine. This article examines the intricate limitations of AI in healthcare and examines fundamental cases that detail the threats to its rapid introduction. Further, it suggests forward-thinking options that focus on the importance of explainable AI, good data stewardship, broad inclusion in design, and responsive regulatory oversight. The article identifies these limitations and argues for pragmatic responses, calling for a transparent, ethical, and contextually informed position on AI, the endgame is to make AI a reliable tool for equitable and high-quality health outcomes.

IOSR Journals, 2025

Introduction: Artificial Intelligence (AI) increasingly influences modern healthcare, offering advancements in diagnostics, treatment, and administrative efficiencies. However, integrating AI into allied healthcare disciplines requires foundational understanding, practical skills, and ethical awareness. In this haste evolution of AI, there arises many critical ethical questions, particularly regarding the implications of relying solely on measurable data. This study explores the ethical considerations associated with AI in medicine and how allied healthcare students can be effectively prepared for AI deployment in their work. Aims and Objectives: This study aims to analyze how future allopathic medical practitioners perceive the ethical concerns of AI deployment in healthcare, specifically their view on AI-driven modalities like x-ray machines, CTscan machines, 5D USG, ECG, Treadmills, etc. compared to the role of technician before and after AI integration. Methodology: a deductive systematic literature review was conducted proceeding the gain of perspective from conference discussion of medical professionals and future practitioners in NCISRT'24. The thorough review inclusion criteria resulted in 50 papers, all mentioned in the reference list, which were used for analysis to support our perspective. Study Analysis A thorough analysis of existing literature and expert opinions from the conference discussions revealed key themes in AI adoption in healthcare: • The growing reliance on AI-driven diagnostic tools, such as radiological image interpretation and predictive analytics. • The necessity of AI literacy among healthcare practitioners to ensure safe and effective AI integration. • The importance of ethical AI governance, addressing issues such as data privacy, algorithmic bias, and transparency. • The need for structured AI training in allied healthcare curricula to ensure a balanced approach to human-AI collaboration. Results • AI has significantly enhanced medical imaging, predictive analytics, and clinical decision support. • A lack of AI literacy among allied healthcare students may hinder its ethical and effective adoption. • Ethical concerns such as bias in AI models and accountability in AI-driven errors require structured frameworks. • Incorporating AI ethics and practical training into medical curricula is essential for preparing future practitioners. Conclusion AI and deep learning technologies hold immense potential to revolutionize medicine, particularly in personalized care, predictive analytics, and automated decision-making. However, their integration into healthcare must be managed with a strong ethical framework. Ensuring transparency and explain ability of AI systems, defining clear lines of responsibility, incorporating diverse datasets, and implementing continuous monitoring are crucial steps. These measures are essential to mitigate ethical risks and safeguard patient welfare, ensuring that AI advancements enhance rather than compromise the integrity of healthcare systems.

2024

Artificial intelligence's impact on healthcare is undeniable. What is less clear is whether it will be ethically justifiable. Just as we know that AI can be used to diagnose disease, predict risk, develop personalized treatment plans, monitor patients remotely, or automate triage, we also know that it can pose significant threats to patient safety and the reliability (or trustworthiness) of the healthcare sector as a whole. These ethical risks arise from (a) flaws in the evidence base of healthcare AI (epistemic concerns); (b) the potential of AI to transform fundamentally the meaning of health, the nature of healthcare, and the practice of medicine (normative concerns); and (c) the 'black box' nature of the AI development pipeline, which undermines the effectiveness of existing accountability mechanisms (traceability concerns). In this chapter, we systematically map (a)-(c) to six different levels of abstraction: individual, interpersonal, group, institutional, sectoral, and societal. The aim is to help policymakers, regulators, and other high-level stakeholders delineate the scope of regulation and other 'softer' Governing measures for AI in healthcare. We hope that by doing so, we may enable global healthcare systems to capitalize safely and reliably on the many life-saving and improving benefits of healthcare AI.

in Constantino Pereira Martins (Coord.), Technopolitics: charting the unknown, 2024

2025

Artificial Intelligence (AI) is increasingly recognized as a transformative force in contemporary medicine, offering solutions to longstanding challenges in healthcare delivery. Defined by its capacity to learn, reason, and solve problems, AI leverages vast datasets and machine learning algorithms to assist in clinical decision-making. Its applications span a wide range of specialties, with particularly notable success in image-based diagnostics such as radiology, dermatology, and cardiology. This article examines the benefits of AI integration in medicine, including improved diagnostic accuracy, enhanced workflow efficiency, and potential contributions to precision medicine. Illustrative cases-such as AI-assisted identification of tethered cord syndromeunderscore its real-world clinical impact. Nonetheless, the adoption of AI presents significant ethical, legal, and operational concerns. These include algorithmic bias, data privacy issues, lack of diversity in training datasets, and questions of accountability in AI-related errors. Furthermore, disparities in access to AI technologies risk exacerbating existing inequalities in healthcare. The article concludes by emphasizing the need for robust regulatory frameworks, interdisciplinary training programs, and equitable implementation strategies to ensure AI contributes positively to future medical practice. As the field evolves, fostering public trust and clinician confidence will be essential in realizing AI's full potential in medicine.

AI and Ethics, 2023

Since uncertainty is a major challenge in medicine and bears the risk of causing incorrect diagnoses and harmful treatment, there are many efforts to tackle it. For some time, AI technologies have been increasingly implemented in medicine and used to reduce medical uncertainties. What initially seems desirable, however, poses challenges. We use a multimethod approach that combines philosophical inquiry, conceptual analysis, and ethical considerations to identify key challenges that arise when AI is used for medical certainty purposes. We identify several challenges. Where AI is used to reduce medical uncertainties, it is likely to result in (a) patients being stripped down to their measurable data points, and being made disambiguous. Additionally, the widespread use of AI technologies in health care bears the risk of (b) human physicians being pushed out of the medical decision-making process, and patient participation being more and more limited. Further, the successful use of AI requires extensive and invasive monitoring of patients, which raises (c) questions about surveillance as well as privacy and security issues. We outline these several challenges and show that they are immediate consequences of AI-driven security efforts. If not addressed, they could entail unfavorable consequences. We contend that diminishing medical uncertainties through AI involves a tradeoff. The advantages, including enhanced precision, personalization, and overall improvement in medicine, are accompanied by several novel challenges. This paper addresses them and gives suggestions about how to use AI for certainty purposes without causing harm to patients.

Computer

he aim of the third United Nations (UN) Sustainable Development Goal, dedicated to "Good Health and Well-Being," is that all people can access the health services they need without facing financial hardship. The goal has three targets: 1) 1 billion more people should benefit from universal health coverage, 2) 1 billion more people should be better protected from health emergencies, and 3) 1 billion more people should enjoy better health and well-being (World Health Organization, 2018). 21 Artificial intelligence (AI) is generally acknowledged as an important component in achieving these three targets. Medical AI dates to the early 1970s, when the AI community was fascinated by the possibilities of applications in the medical domain. 1 The field went through cycles of great achievements, followed by inflated expectation and then huge disappointment ("AI winter"). Due to the big success of statistical data-driven machine learning, we are experiencing another "AI spring," with great enthusiasm and excitement in the medical community. The availability of very large data sets in (precision) medicine and recent developments in statistical machine learning, particularly deep learning, have led to impressive results, with machines