The DOI matches its Crossref record; 166 of the 166 references it cites were found in a registry or an index; 5 of the 141 sources its claims cite were checked for support.
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
Open accessAbstract
In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts. This comprehensive research article rigorously investigates the ethical dimensions intricately linked to the rapid evolution of AI technologies, with a particular focus on the healthcare domain. Delving deeply, it explores a multitude of facets including transparency, adept data management, human oversight, educational imperatives, and international collaboration within the realm of AI advancement. Central to this article is the proposition of a conscientious AI framework, meticulously crafted to accentuate values of transparency, equity, answerability, and a human-centric orientation. The second contribution of the article is the in-depth and thorough discussion of the limitations inherent to AI systems. It astutely identifies potential biases and the intricate challenges of navigating multifaceted contexts. Lastly, the article unequivocally accentuates the pressing need for globally standardized AI ethics principles and frameworks. Simultaneously, it aptly illustrates the adaptability of the ethical framework proposed herein, positioned skillfully to surmount emergent challenges.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This article reviews ethical dimensions of AI in healthcare and beyond, arguing for transparency, equity, accountability, and human-centric design, and proposes a six-pillar ethical framework plus governance/collaboration components. It catalogs AI limitations such as black-box opacity, bias, limited contextual and emotional intelligence, and trust/accountability challenges, while calling for globally standardized AI ethics principles.
Key findings
- Proposes a conscientious AI framework emphasizing transparency, equity, answerability, and human-centric orientation, with six pillars: Sensitivity, Evaluation, User-Centricity, Responsibility, Beneficence, and Security.
- Identifies major AI limitations: black-box/explainability problems, bias and fairness concerns (statistical, social, systematic, generalization, and human bias), limited contextual intelligence, erosion of human connection/empathy, and challenges to trust, reliability, and accountability.
- Cites illustrative evidence: an algorithm using healthcare spending would reduce the number of Black patients identified as needing extra care by over 50%; a 2018 London Metropolitan Police facial recognition system identified 104 previously unknown suspects but only 2 were accurately predicted; GPT-4 as a medicine chatbot offers benefits but risks misinformation, overreliance, ethical/legal concerns, and lack of emotional support.
- Calls for globally standardized AI ethics principles and frameworks, and outlines a governance/collaboration ecosystem including Human Oversight and Intervention (HOI), Multi-Stakeholder Engagement (MSE), Privacy by Design (PbD), Safe AI, Ethical Governance Council (EGC), Awareness Programs, Continuous Improvement and Adaptability (CIA), and Inter-Generational Considerations (IGC).
Methods
- Design: comprehensive research article and conceptual review/framework proposal, not an empirical study.
- Data or sample: no primary dataset or sample is reported; the paper draws on literature, examples, and legal frameworks such as GDPR and HIPAA.
- Main analysis: thematic discussion of ethical dimensions, AI limitations, data privacy/consent, and the proposed six-pillar ethical framework and governance/collaboration ecosystem.
Limitations
- The paper states limitations inherent to AI systems—bias, black-box opacity, contextual intelligence, lack of empathy, and trust/accountability challenges—but does not state limitations of its own framework.
- Evident from the text: the proposed framework is conceptual and not empirically validated; no systematic review protocol, sample, or quantitative evaluation is reported.
- Evident from the text: examples are illustrative and drawn from secondary sources, with no primary data collection or validation of the proposed pillars.
Model-written summary. Check it against the paper before relying on it.
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Cites
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Verification
Claims the paper makes
120 Not checked yet · 21 Own interpretation · 11 No basis given · 3 Partly supported · 1 Couldn’t confirm · accuracy not yet measured
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“While these tools can streamline communication and provide quick responses, they lack the emotional depth and understanding of human healthcare providers.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. EROSION OF HUMAN CONNECTION AND EMPATHY
What the sources say
- Could not verifycites [132] sourceExperimental — accuracy not yet measured
The Literature review (§2) describes chatbots as simulating human conversation, and §2.5 describes a virtual nurse designed to feel like talking to a person. Neither section says chatbots lack the emotional depth or understanding of human healthcare providers. Searches for empathy, emotions, and limitations relative to human doctors found no passage addressing that claim; page numbers were unavailable. (LLM, conf=0.76)
plain- Asserted: passes — The authors put it forward as true.
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- Could not verify
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In the paper
“The Food and Drug Administration (FDA) regulatory approach to AI in healthcare, for example, seeks to ensure that AI applications meet the same safety and effectiveness standards as traditional medical devices.This approach ensures that AI systems, such as those used for diagnosing diseases or making treatment recommendations, are held accountable for their performance and outcomes [143].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > D. TRUST IN AI
What the sources say
- Partly supportedcites [143] sourceExperimental — accuracy not yet measured
The Introduction states that FDA approval requires compliance with standards and evaluation of device safety and efficacy; the Discussion says the FDA assesses medical devices on the same basis regardless of ML use. But the paper does not say this approach ensures AI systems are held accountable for their performance and outcomes. Its Discussion instead notes that responsibility for clinical decisions often remains with clinicians and that questions about responsibility require further examination (no page numbers provided). (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Partly supported
None of the sources cited for this claim have been checked yet.
In the paper
“However, despite these advancements, certain limitations hinder the widespread adoption and integration of AI into clinical practice, particularly in terms of explainability and interpretability [3], [4].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
What the sources say
- Not checked yetcites [3] source
- Not checked yetcites [4] Karim, 2018, Machine learning interpretability: A science rather than a tool, arXiv:1807.06722
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Insufficient interpretability hinders the widespread and responsible adoption of ML, especially in high-stakes domains where transparency and accountability are paramount.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
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None of the sources cited for this claim have been checked yet.
In the paper
“In the healthcare context, the lack of interpretability [10] and explainability [11] in ML and DL models can lead to ethical issues and a loss of trust [12], [13].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By integrating transparency and interpretability into AI models, these systems can instill trust and confidence among healthcare professionals and patients.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“However, the inner workings of these models are often complex and challenging to explain in human terms [38], [39].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > II. BLACK BOX AND LACK OF TRANSPARENCY
What the sources say
- Not checked yetcites [38] Bathaee, 2017, The artificial intelligence black box and the failure of intent and causation, Harv. JL Tech.
- Not checked yetcites [39] source
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The black-box nature of AI models restricts the transparency of their outputs, making it difficult to comprehend the reasoning behind their decisions [18] as illustrated in Figure 1.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
What the sources say
- Not checked yetcites [18] Lakkaraju, 2017, Interpretable & explorable approximations of black box models, arXiv:1707.01154
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The absence of explainability in AI systems also raises ethical concerns.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Moreover, it can also facilitate regulatory compliance by enabling audits and assessments of AI models to ensure fairness, accountability, and compliance with ethical standards [29], [30].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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- It may state two separate claims as one. 36
- Probably not a substantial claim. 43
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In 2020, an AI-powered selfdriving car was involved in a fatal accident.The car was unable to understand the context of the situation and it collided with a pedestrian [100].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > A. CONTEXTUAL INTELLIGENCE
What the sources say
- Partly supportedcites [100] sourceExperimental — accuracy not yet measured
Section 2.1 reports that an Uber self-driving vehicle fatally struck a pedestrian, but dates the collision to March 18, 2018—not 2020. The “Automated driving” section says its system detected the pedestrian but failed to classify her correctly or predict her path, supporting the claim’s general description of the failure. (LLM, conf=0.91)
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In organ transplantation, AI could assist in matching donors with recipients efficiently.However, ethical nuances such as patient preferences, medical histories, and urgency require human judgment [124], [125].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > 1) Human-AI interaction
What the sources say
- Partly supportedcites [124] sourceExperimental — accuracy not yet measured
The Introduction (AI for Transplant; page unavailable) says AI may improve donor–recipient matching, while ethical dilemmas mean transplant decisions should retain a human decision-maker. The Discussion (Toward Meaningful Use of AI in Transplant; page unavailable) discusses patient involvement, but the paper does not identify patient preferences, medical histories, and urgency as the specific ethical considerations requiring human judgment in matching. (LLM, conf=0.86)
- Partly supportedcites [125] sourceExperimental — accuracy not yet measured
The organ-allocation section says AI could improve donor–recipient matching and that allocation considers expected outcome and emergency. It does not say that patient preferences, medical histories, or urgency require human judgment; instead, it suggests AI could strengthen allocation decisions. The human-judgment qualification attached to this citation is therefore not supported. (LLM, conf=0.78)
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
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None of the sources cited for this claim have been checked yet.
In the paper
“Artificial intelligence (AI) has shown immense promise in transforming healthcare through the application of advanced technologies like machine learning (ML) and deep learning (DL) [1], [2].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The potential for biases embedded within algorithms to exacerbate health disparities and perpetuate social injustices underscores the necessity of vigilance in algorithm development.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“XAI aims to enhance the transparency and interpretability of AI models, providing insights into the decision-making process and enabling clinicians to understand the reasoning behind AI-generated recommendations [23].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
What the sources say
- Not checked yetcites [23] source
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Biased data or biased model predictions can have serious consequences in healthcare, resulting in inaccurate diagnoses, suboptimal treatments, and potential harm to the patients.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
In the paper
“The application of AI in healthcare brings forth several limitations that need to be addressed for its successful integration into clinical practice [37].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
What the sources say
- Not checked yetcites [37] source
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“The rapid evolution of AI technologies has outpaced the formulation of comprehensive ethical guidelines, raising concerns about the potential misuse and unintended consequences of these powerful tools [32], [33].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In the realm of healthcare, these concerns encompass a wide spectrum of issues, including data privacy and security, algorithmic bias, accountability, and the potential dehumanization of patient care.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Techniques such as LIME [25] and SHAP [26] are examples of post-hoc explainability methods commonly used in healthcare.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
What the sources say
- Not checked yetcites [25] source
- Not checked yetcites [26] Lundberg, A unified approach to interpreting model predictions
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In the paper
“The need for an ethical framework becomes even more pronounced as AI is poised to extend its influence beyond healthcare into various domains, such as education, transportation, and governance [34].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
What the sources say
- Not checked yetcites [34] source
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In the paper
“Without explainability, healthcare professionals may find it challenging to trust and validate the outputs of AI systems, leading to resistance to adopting these technologies [19].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > I. INTRODUCTION
What the sources say
- Not checked yetcites [19] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, understanding how these models arrive at their predictions is often elusive due to their black-box nature.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > II. BLACK BOX AND LACK OF TRANSPARENCY
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In the paper
“Bias can be categorized as statistical or social, and it can lead to unequal outcomes and hinder equitable healthcare provision [53], [54].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > III. BIAS AND FAIRNESS
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In the paper
“Black box models might struggle to explain why a particular intervention is recommended for a specific individual, making it challenging to adapt and personalize interventions effectively [50].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > II. BLACK BOX AND LACK OF TRANSPARENCY
What the sources say
- Not checked yetcites [50] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Cognitive biases and the lack of diversity in the training data contribute to inter-and intra-scoring disagreement, posing limitations [63].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > III. BIAS AND FAIRNESS
What the sources say
- Not checked yetcites [63] source
plaindirect- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Data limitations pose a critical challenge in healthcare AI, as biased datasets can result in biased algorithmic outputs.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > III. BIAS AND FAIRNESS
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“However, cardiovascular disease manifests differently in men and women, and an algorithm primarily trained on male data may not accurately diagnose female patients [59].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > III. BIAS AND FAIRNESS
What the sources say
- Not checked yetcites [59] Jameson, 2018
hedgedconditional- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These biases can further perpetuate health inequities and hinder the potential benefits of AI in healthcare.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > III. BIAS AND FAIRNESS
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In the paper
“In healthcare, systematic biases in algorithms can have far-reaching effects on patient care, health outcomes, and equitable access to healthcare services.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. SYSTEMATIC BIAS
plainconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Therefore, data can be susceptible to various types of bias, which can significantly impact healthcare outcomes while fairness in AI focuses on ensuring that the outcomes and decisions produced by AI systems do not discriminate against specific groups or individuals based on their inherent characteristics, such as race, gender, age, or socioeconomic status.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > III. BIAS AND FAIRNESS
plainconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“Clinical trials often have predominantly male participants from limited age groups and similar ethnic backgrounds, leading to gender and ethnic biases.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > III. BIAS AND FAIRNESS
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“for instance the data collected in [80] does not contain the images from different ethnicities which will hinder models generalization.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. GENERALIZATION BIAS
What the sources say
- Not checked yetcites [80] source
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“So, the system was found to be biased against people of color.This happened as the system was trained on a dataset that was not representative of the population of London.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > A. CONTEXTUAL INTELLIGENCE
What the sources say
- Not checked yetcites [99] Santow, 2020, Can artificial intelligence be trusted with our human rights?, AQ, Austral. Quart.
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
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In the paper
“AI models, particularly those based on statistical learning, might struggle with common-sense reasoning, making them less effective in handling unfamiliar or ambiguous situations [97].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > A. CONTEXTUAL INTELLIGENCE
What the sources say
- Not checked yetcites [97] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“The increasing integration of AI in healthcare has raised concerns about the potential erosion of human connection and empathy in patient care.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. EROSION OF HUMAN CONNECTION AND EMPATHY
What the sources say
- Not checked yetcites [126] Kamensky, 2019, Artificial intelligence and technology in health care: Overview and possible legal implications, DePaul J. Health Care L
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“For instance, in 2019, Amazon's Alexa was criticized for not being accessible to people with disabilities.The voice assistant's responses were not always clear or understandable, and the assistant's features were not always compatible with screen readers [107].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. EQUITY AND ACCESS
What the sources say
- Not checked yetcites [107] source
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In the paper
“While the robot's responses are programmed, the integration of EI principles will enhance the user experience [131].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. EROSION OF HUMAN CONNECTION AND EMPATHY
What the sources say
- Not checked yetcites [131] source
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI's role is to facilitate and augment the decision-making process, but it cannot replace the ethical reflection and empathy that human professionals bring to the table.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > 1) Human-AI interaction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, the radiologists' years of training and experience are essential in validating the findings, understanding clinical context, and making informed diagnostic decisions.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > 1) Human-AI interaction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“These challenges include AI's ability to understand complex language, grasp contextual nuances, and provide transparent explanations for its decisions [109], [110].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > 1) Human-AI interaction
What the sources say
- Not checked yetcites [109] Xu, 2021, Transitioning to human interaction with AI systems: New challenges and opportunities for HCI professionals to enable human-centered AI, arXiv:2105.05424
- Not checked yetcites [110] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“While many users found it helpful to have a tool they could access anytime [133].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. EROSION OF HUMAN CONNECTION AND EMPATHY
What the sources say
- Not checked yetcites [133] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, AI lacks true empathy, relying on algorithms and data patterns to assess patient conditions and recommend treatments.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. EROSION OF HUMAN CONNECTION AND EMPATHY
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“For instance, AlphaFold, an AI-powered algorithm, successfully solved the long-standing problem of protein folding, which had hindered progress in biology and medicine for decades.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > V. ETHICAL CONCERNS AND VALUE ALIGNMENT
What the sources say
- Not checked yetcites [144] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“limitation can hinder its ability to respond effectively to patients' emotional needs and concerns, potentially leaving them feeling unheard or emotionally unsupported.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. EROSION OF HUMAN CONNECTION AND EMPATHY
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“It was also reported feeling disconnected and frustrated with the lack of genuine human interaction.Some users expressed that the chatbot's responses, although based on evidence-based techniques, felt robotic and impersonal, leading to a sense of isolation [134].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. EROSION OF HUMAN CONNECTION AND EMPATHY
What the sources say
- Not checked yetcites [134] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Patients may feel a sense of detachment and isolation when their interactions become solely AI-mediated, potentially reducing the personalized and empathetic care they receive.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. EROSION OF HUMAN CONNECTION AND EMPATHY
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“GDPR Article 5-1b draws a vital link between the purpose of data processing and its legality.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites GDPR Article 5-1b
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Healthcare datasets primarily consist of patient information, and strict regulation of medical privacy, encompassing the security of medical records and the confidentiality of conversations between healthcare professionals [145], [146].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > V. ETHICAL CONCERNS AND VALUE ALIGNMENT
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Without XAI, it becomes challenging to ensure ethical decision-making, informed consent, and accountability for AI-driven recommendations.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > V. ETHICAL CONCERNS AND VALUE ALIGNMENT
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“Data accuracy, an essential facet emphasized in GDPR Article 5-1d, has become paramount in the AI landscape.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites GDPR Article 5-1d
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“2) Granularity of Consent Criterion: Consent for profiling must be distinct and separate from granting access to a service.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > A. PATIENTS CONSENT FOR DATA
What the sources say
- Not checked yetcites [152] 2016
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“GDPR applies not only within the EU and European Economic Area (EEA) but also to the transfer of personal data outside these regions.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > V. ETHICAL CONCERNS AND VALUE ALIGNMENT
What the sources say
- Not checked yetcites [148] 2016
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The potential applications of AI in healthcare are vast, including rapid diagnosis, personalized care, and reduction of unnecessary outpatient visits, resulting in significant cost savings [144].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > V. ETHICAL CONCERNS AND VALUE ALIGNMENT
What the sources say
- Not checked yetcites [144] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Under GDPR's Article 5 [155], 5-1a, the focus is on transparent and lawful data processing.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites GDPR Article 5-1a
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The GDPR grants EU residents the right to request search engines to remove personal information associated with their names from search results.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > V. ETHICAL CONCERNS AND VALUE ALIGNMENT
What the sources say
- Not checked yetcites [148] 2016
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“In 2018, The General Data Protection Regulation (GDPR) [148] replaced the Data Protection Directive (DPD) in the European Union (EU), establishing comprehensive data protection and privacy regulations.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > V. ETHICAL CONCERNS AND VALUE ALIGNMENT
What the sources say
- Not checked yetcites [148] 2016
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“GDPR Article 5-1e stresses the necessity of limiting the retention of personal data to what is essential for processing.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites GDPR Article 5-1e
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Similar concerns are also discussed in [149], which encompass patient privacy and confidentiality, challenges in obtaining informed consent, limited data ownership, potential inaccuracies, biases, commercialization risks, and security vulnerabilities.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > V. ETHICAL CONCERNS AND VALUE ALIGNMENT
What the sources say
- Not checked yetcites [149] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“1) Specific Consent Criterion: Consent must allow further processing only if it aligns with a legal basis and remains consistent with the original purpose for data collection.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > A. PATIENTS CONSENT FOR DATA
What the sources say
- Not checked yetcites [152] 2016
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“From the viewpoint of healthcare, securing valid consent is crucial when engaging in AI-powered data analytics and profiling, as outlined by GDPR Article 6.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > A. PATIENTS CONSENT FOR DATA
What the sources say
- Not checked yetcites GDPR Article 6
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“The Health Insurance Portability and Accountability Act (HIPAA) [147] was passed in 1996 to strengthen healthcare data protection by the US government.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > V. ETHICAL CONCERNS AND VALUE ALIGNMENT
What the sources say
- Not checked yetcites [147] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Furthermore, GDPR Article 5-1f introduces the security principle, mandating the safeguarding of data integrity and confidentiality during processing.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites GDPR Article 5-1f
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Covered entities are generally prohibited from using or disclosing PHI without individual authorization, except in specific cases outlined by the rule.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites [157] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Individuals have the right to access their PHI, request amendments, and receive an accounting of disclosures.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites [157] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This rule defines Protected Health Information (PHI) as any data, in any format, that can identify an individual based on their health, care, or payment history § §164.501& 160.103.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites §164.501& 160.103
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Covered entities must have contracts with their business associates, who handle PHI on their behalf.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites [157] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“HIPAA's Privacy Rule §164.514establishes guidelines for the handling of PHI in the US healthcare system.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites [157] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“These exceptions include treatment, payment, healthcare operations, public health activities, and law enforcement purposes.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites [157] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The rule emphasizes the principle of using only the minimum necessary information for a given purpose, and it addresses incidental uses and disclosures.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites [157] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Article 21's right to object enables individuals to halt data processing, ensuring data minimization and protecting against data misuse.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. PATIENTS' RIGHTS TO THE DATA
What the sources say
- Not checked yetcites [161] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This principle dictates that any communication about a patient should only include the least amount of information needed for its purpose §164.502[b-1].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites §164.502[b-1]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Moreover, it also guarantees that privacy, accuracy, and accessibility of all electronic PHI under a covered entity's care, transmission, or storage defends against foreseeable misuses or exposures of ePHI, and ensures that its staff complies with these standards §164.306[b][159].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites §164.306[b][159]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“If all identifying details are stripped from the data, it is no longer considered PHI, and the restrictions do not apply §164.514[a].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites §164.514[a]
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Healthcare providers can use or disclose PHI for these purposes, and while they can obtain consent for each disclosure related to treatment, it is not mandatory §164.506[b-1][158].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. DATA PRIVACY AND SECURITY
What the sources say
- Not checked yetcites §164.506[b-1][158]
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Moreover, HIPAA and GDPR do not directly mention AI, several of its clauses have relevance to AI applications and can be challenged by new ways that AI processes personal data [165], [166].”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. PATIENTS' RIGHTS TO THE DATA
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Article 22-4 prohibits using sensitive data for automated decisions, with an exception.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. PATIENTS' RIGHTS TO THE DATA
What the sources say
- Not checked yetcites [161] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Yet, AI can infer sensitive data from non-sensitive data, potentially leading to unauthorized processing.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > C. PATIENTS' RIGHTS TO THE DATA
What the sources say
- Not checked yetcites [161] source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper relies on this without support.
The deployment of deep learning AI methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts.
“In the past decade, the deployment of deep learning (Artificial Intelligence (AI)) methods has become pervasive across a spectrum of real-world applications, often in safety-critical contexts.”
The paper relies on this without support.
In healthcare, decisions can have life-and-death consequences.
“In healthcare, where decisions can have life-and-death consequences, it is crucial to have a clear understanding of how AI algorithms arrive at their recommendations.”
The paper relies on this without support.
Evidence-based medicine has long been the gold standard for treatment strategies.
“Evidence-based medicine has long been the gold standard for treatment strategies.”
The paper relies on this without support.
The size and complexity of models, particularly deep neural networks, have increased in pursuit of better predictive performance.
“The size and complexity of models, particularly deep neural networks (DNNs), have increased in pursuit of better predictive performance.”
The paper relies on this without support.
Limited generalization of AI models is a critical challenge in deploying AI systems in healthcare.
“One of the critical challenges in deploying AI systems in healthcare is the limited generalization of models and ensuring their safety.”
The paper relies on this without support.
AI models may have limited exposure to rare medical conditions or unique patient cases during training, compromising their ability to accurately diagnose or treat such cases.
“Moreover, AI models may have limited exposure to rare medical conditions or unique patient cases during training.Consequently, their ability to accurately diagnose or treat such cases may be compromised.”
The paper relies on this without support.
The lack of diversity within AI development teams may result in models that fail to adequately address the diverse needs of the users.
“Moreover, the lack of diversity within AI development teams may result in models that fail to adequately address the diverse needs of the users.”
The paper relies on this without support.
Cost and affordability pose barriers, hindering access to AI advancements for those with limited financial resources.
“Additionally, cost and affordability pose barriers, hindering access to AI advancements for those with limited financial resources.”
The paper relies on this without support.
Cultural and language barriers can restrict the inclusivity and effectiveness of AI systems.
“Cultural and language barriers can also restrict the inclusivity and effectiveness of AI systems.”
The paper relies on this without support.
AI's limited learning from user interactions and difficulty in handling uncertainty and ambiguity pose obstacles to human-AI interaction.
“AI's limited learning from user interactions and difficulty in handling uncertainty and ambiguity also pose obstacles.”
The paper relies on this without support.
Users may develop over-reliance on AI, leading to frustration and reduced trust when AI fails to meet their expectations.
“Users may develop over-reliance on AI, leading to frustration and reduced trust when AI fails to meet their expectations.”
The paper relies on this without support.
Implementing transparent and XAI models fosters trust and accountability.
“Implementing transparent and XAI models fosters trust and accountability.”
The paper relies on this without support.
The effectiveness of privacy protection laws in practice varies.
“However, the effectiveness of these laws in practice varies.”
The paper relies on this without support.
Automated decision-making, profiling, and the utilization of ML techniques are reshaping data processing, yet they bring forth concerns of bias and privacy invasion.
“Automated decision-making, profiling, and the utilization of ML techniques are reshaping data processing, yet they bring forth concerns of bias and privacy invasion.”
The paper relies on this without support.
AI algorithms often process vast amounts of health data to generate insights and recommendations.
“AI algorithms often process vast amounts of health data to generate insights and recommendations.”
The paper relies on this without support.
AI has the potential to perpetuate biases and stereotypes.
“With AI's potential to perpetuate biases, this pillar advocates systems to perpetuate biases and stereotypes, this pillar supports continuous monitoring and mitigation of biases through data curation, algorithm design, and stakeholder diversity.”
The paper relies on this without support.
AI models can be vulnerable in unique ways beyond traditional cybersecurity.
“This goes beyond traditional cybersecurity, the AI models can be vulnerable in unique ways.”
The paper relies on this without support.
Privacy concerns emerge due to the sensitive nature of data processed by AI systems.
“Privacy concerns emerge due to the sensitive nature of data processed by AI systems.”
The paper relies on this without support.
Economic incentives that drive AI development can profoundly influence its direction.
“Though it is not necessarily an ethical consideration, the economic incentives that drive AI development can profoundly influence its direction.”
The paper relies on this without support.
AI continues to grapple with comprehending the nuances and intricacies of human context.
“While AI has made remarkable strides, it continues to grapple with comprehending the nuances and intricacies of human context.”
The paper relies on this without support.
Understanding complex language and emotional nuances remains challenging as AI and humans interact.
“Additionally, as AI and humans interact, understanding complex language and emotional nuances remains challenging.”
The paper relies on this without support.
Educated decisions can only be made when individuals understand the implications and consequences of AI applications.
“Educated decisions can only be made when individuals understand the implications and consequences of AI applications.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Additionally, ethical concerns around data ownership, informed consent, and algorithmic transparency are growing as AI becomes more pervasive in healthcare [116].”
From Federated Learning in Smart Healthcare: A Comprehensive Review on Privacy, Security, and Predictive Analytics with IoT Integration (2024) · cites this paper as [116]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. SYSTEMATIC BIAS
“Algorithmic Bias: refers to biases that are introduced by the algorithm itself and are not present in the input data.”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > B. SYSTEMATIC BIAS
“For example, if an algorithm is trained on biased or incomplete medical data, it may disproportionately impact certain demographic groups, resulting in disparities in diagnosis or treatment recommendations [76].”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > VI. WAY FORWARD
“this pillar advocates systems to perpetuate biases and stereotypes”
§ Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond > VI. WAY FORWARD
“this pillar supports continuous monitoring and mitigation of biases through data curation, algorithm design, and stakeholder diversity.By fostering sensitivity, AI creators prevent inadvertent reinforcement of societal prejudices”
Citations
Cites
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.74 · Cited by 24,513
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · cited 2×
“For instance, [55] highlighted the significance of this issue through their study, which revealed racial bias in a commonly used algorithm in the U.S. healthcare system.”
Background“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682
Lipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800
(2018) · Cited by 3,510
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- Santow, 2020, Can artificial intelligence be trusted with our human rights?, AQ, Austral. Quart., 91, 10
- Božić, 2023, Artifical intelligence as the reason and the solution of digital divide, Lang. Educ. Technol., 3, 90
- West, 2019, Discriminating systems, AI Now, 1
- Beck, 2017, The rise of AI makes emotional intelligence more important, Harvard Bus. Rev., 15
- 2016, Regulation EU 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons With Regard to the Processing of Personal Data and on the Free Movement of Such Data, and Repealing Directive 95/46/EC (General Data Protection Regulation)
- 2016, Guidelines on Consent Under Regulation 2016/679 (WP259REV. 01)
- 2022, US Department of Health & Human Services
Cited by
Sorted by citations
Abbas, S. R., Abbas, Z., Zahir, A., & Lee, S. W. (2024) · Healthcare · doi:10.3390/healthcare12242587 · Cited by 228
Saeidnia, H. R., Fotami, S. G. H., Lund, B. D., & Ghiasi, N. (2024) · Social Sciences · doi:10.3390/socsci13070381 · Cited by 227
Ethical framework for artificial intelligence in healthcare research: A path to integrity
Abujaber, A. A., & Nashwan, A. J. (2024) · World Journal of Methodology · doi:10.5662/wjm.v14.i3.94071 · Cited by 87
Olawade, D. B., Plabon, S. B., Ojo, A., Ogunbona, M. A., Makanjuola, B. D., & Olasilola, R. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106362 · Cited by 93
Human centred explainable AI decision-making in healthcare
van Leersum, C. M., & Maathuis, C. (2025) · Journal of Responsible Technology · doi:10.1016/j.jrt.2025.100108 · Cited by 66
Metadata
- Authors
- Nasir, S., Khan, R. A., & Bai, S.
- Affiliations
- Salim Habib University (PK)
- Venue
- IEEE Access
- Published
- 2024
- Volume
- 12
- Pages
- 31014-31035
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4392172846
- Semantic Scholar
- 5e38fb3c018d4476549d993d38926db2e5c6032a
- Cited by
- 152
- References
- 158
- Citations by year
- 2026: 64 · 2025: 60 · 2024: 28
- FWCI
- 62.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- arXiv (Cornell University) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- AI ethics, ethical frameworks, artificial intelligence in healthcare, deep learning, transparency, data management, human oversight, artificial intelligence in education, international cooperation, algorithmic bias, equity, accountability, safety-critical systems
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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