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Gomes Lima Junior, M.F. Lucena Karbage, P.A. Nascimento" "autores" => array:3 [ 0 => array:3 [ "nombre" => "A." "apellidos" => "Gomes Lima Junior" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0005" ] ] ] 1 => array:4 [ "nombre" => "M.F." "apellidos" => "Lucena Karbage" "email" => array:1 [ 0 => "mariafernandalk@gmail.com" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0010" ] 1 => array:2 [ "etiqueta" => "*" "identificador" => "cor0005" ] ] ] 2 => array:3 [ "nombre" => "P.A." "apellidos" => "Nascimento" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">c</span>" "identificador" => "aff0015" ] ] ] ] "afiliaciones" => array:3 [ 0 => array:3 [ "entidad" => "Doctor en Medicina, Posgrado en el Hospital Israelita Albert Einstein Sao Paulo SP, Brasil, Coordinador Científico del Sector de Neurorradiología del Hospital Antonio Prudente, Fortaleza, Ceará, Brazil, Maestría en Ciencias en el Departamento de Investigación Clínica Icahn School of Medicine en Mount Sinai, New York, USA" "etiqueta" => "a" "identificador" => "aff0005" ] 1 => array:3 [ "entidad" => "Estudiante de Medicina, Facultad de Medicina, Unichristus University, Fortaleza, Ceará, Brazil" "etiqueta" => "b" "identificador" => "aff0010" ] 2 => array:3 [ "entidad" => "Doctor en Medicina, Médico residente en radiología, Hospital Antonio Prudente, Fortaleza, Ceará, Brazil" "etiqueta" => "c" "identificador" => "aff0015" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Una actualización sobre aspectos éticos en la investigación clínica: el abordaje de cuestiones sobre el desarrollo de nuevas herramientas de IA en radiología" ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0030">Introduction</span><p id="par0005" class="elsevierStylePara elsevierViewall">In health research, ethics involves a set of principles and norms that must be constantly updated and reassessed to ensure that it is valid, reliable, legitimate, and representative.<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">Digital technology is transforming how healthcare professionals work, including radiology, where AI tools are increasingly used to automate labor-intensive tasks like examining CT images. This shift requires radiologists to adapt and focus on more complex cognitive tasks.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2–5</span></a></p><p id="par0015" class="elsevierStylePara elsevierViewall">Although AI-based technology and its application in healthcare are expanding, the paucity of ethical aspects in AI research remains a significant issue. The current published literature lacks practical tools for testing and upholding ethical requirements across the lifecycle of AI-based technologies. Besides, its enforcement in public health raises ethical concerns related to privacy, trust, accountability, and numerous biases.<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">In this article, by reviewing the existing literature, we aim to identify and discuss some ethical issues that arise with the development and use of AI algorithms in radiology, such as the ethics of data, algorithms, practice, and conflicts of interest, by analyzing current research and contributing to the ongoing discussion on AI ethics in healthcare and promoting the responsible use of AI technology in radiology.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0035">The ethics of data</span><p id="par0025" class="elsevierStylePara elsevierViewall">The radiology domain contains a substantial amount of patient data. The ethics of data surrounds the acquisition, management, and assessment of data. Some of the most important areas of data ethics to consider include informed consent, ownership of data, transparency, objectivity, privacy/data protection, ensuring moral and meaningful access to data, and the resource to ensure data management.<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1–3,7</span></a> Indeed, researchers and radiologists have a moral obligation to utilize patient information to improve radiology practice and patient care.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,3,7</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">Unfortunately, there are ways that data can be unethically used, in particular for commercial purposes.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,3</span></a> In addition, well-labeled and high-quality data, required along and after algorithm training, are highly fetched, and their value is skyrocketing.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p><p id="par0035" class="elsevierStylePara elsevierViewall">Certainly, it is imperative to consider inquiries regarding patient data ownership.<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3,4</span></a> For instance, who owns the data that can lead to the creation of highly profitable intelligence products? Also, who owns the intellectual property of the analysis that emerges from aggregated data?<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,4,7</span></a></p><p id="par0040" class="elsevierStylePara elsevierViewall">Patient autonomy over their data is a crucial point of discussion. Most patients agree, through consent forms, to the retrospective use of data for research purposes. In fact, in Europe, the General Data Protection Regulation (GDPR) allows consent withdrawal at any time and requires patients' permission for information reuse.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> Data ownership definitions vary significantly among countries, limiting the answers about data's commercial use beneficiaries.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> Ultimately, patients own their data, so should they participate in the profits made by AI systems built through the given information? Or should the companies fully hold the interests once they buy the rights to medical data access? Foremost, could patients choose to use their data exclusively for academic ends? Or even pick the company they want to sell their information to?</p><p id="par0045" class="elsevierStylePara elsevierViewall">Deeper discussions are needed to understand commercial and academic data practices to create policies that balance benefits with the greater good without harming patients.</p></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0040">Data collection and management</span><p id="par0050" class="elsevierStylePara elsevierViewall">AI systems greatly depend on the quality and amount of data used to develop them.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> Although, there are significant barriers related to radiology data collection, annotation process, availability, and accessibility. This scenario leads to data scarcity and impairs deep learning (DL) -powered tools to subsidize radiologists' decisions.<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8–10</span></a></p><p id="par0055" class="elsevierStylePara elsevierViewall">Representative and high-quality data require researchers' continuous monitoring along the data extraction steps.<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8,11</span></a> Concerning this remark, automated data extraction systems showed flawed extraction ability once the scarcity of standardized data and contextual and user-specific variability proposes challenges to its performance.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p><p id="par0060" class="elsevierStylePara elsevierViewall">Indeed, the radiology field is significantly prejudiced by technical acquisition factors bias due to divergences between different machines or acquisition methods.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> While radiologists are used to interpreting technical differences in imaging, such as slice thickness or scanner brand,</p><p id="par0065" class="elsevierStylePara elsevierViewall">DL- systems could better execute this ability if they were exposed to these variables during the training phase.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p><p id="par0070" class="elsevierStylePara elsevierViewall">Bias can also arise when handling patient data.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2–4,7</span></a> As aforementioned, data sharing is fundamental to diverse image datasets.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> Although, other issues with the implementation of widespread data sharing include data access policies, data quality and safety policies, intellectual property issues, and data protection.<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a></p><p id="par0075" class="elsevierStylePara elsevierViewall">Besides, ethical impasses emerge due to imaging reconstruction technologies, especially regarding facial exams, which violate patients' privacy.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a> Nowadays, facial recognition to 3D reconstruction can build models from unidentified medical images, such as MRIs, thereby unraveling anonymization.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> However, the existing re-identification prevention software is limited to specific DL systems and data types, hindering its application to most algorithms.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0045">The ethics of algorithms</span><p id="par0080" class="elsevierStylePara elsevierViewall">Decision-making is part and parcel of medicine and healthcare. It involves selecting a course of action from different alternatives.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> People make decisions using their beliefs, knowledge, preferences, and values.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> AI makes a decision depending on the features of input data.<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3,4</span></a> Human values, beliefs, and preferences will often be transferred to AI, yet it is the source of human bias.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,3</span></a> Although AI products are not human, they are envisioned, built, and evaluated by them.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,3</span></a> Therefore, human concepts are responsible for equality and fairness.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> In other words, humans can misuse AI models.<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3,4</span></a> Therefore, it is imperative to ensure transparency in how decisions are made to promote provider and patient trust in AI.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,3,5,12</span></a></p><p id="par0085" class="elsevierStylePara elsevierViewall">Furthermore, AI use promotes the "automation bias," meaning that humans start to rely entirely on the work of a machine instead of applying their critical judgment and scrutiny. Therefore, patients become more vulnerable to AI's mistakes if health decision-making gets based on physicians' trust in unverified AI conclusions.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a></p><p id="par0090" class="elsevierStylePara elsevierViewall">Particularly in insurance-based countries, AI systems could prejudicate multiple health system users.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> Therefore, AI practical application requires constant and assertive analysis to prevent algorithms' decisions from being accepted over doctors' moral and knowledge-guided intuition.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0050">The ethics of practice</span><p id="par0095" class="elsevierStylePara elsevierViewall">AI in radiology is complex because it combines clinical care, business, economics, technology, and mathematics.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> Nevertheless, moral behavior is intellectually uncertain.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a> Moreover, there are instances where innovations unintentionally cause harm and engage in unprincipled activities.<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">7</span></a> Therefore, there is a need to engage in moral and ethical values when deciding where to involve AI, define what responsible AI ought to be, and raise the alarm when AI behaves unethically.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a></p><p id="par0100" class="elsevierStylePara elsevierViewall">The sampling bias occurs when curated data needs to appropriately represent the population, primarily due to data collection barriers.<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8,11</span></a> This concern occurs when a single institution provides the data to develop and train the DL algorithms, resulting in discrimination of underrepresented subsets of other institutions' populations.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p><p id="par0105" class="elsevierStylePara elsevierViewall">Unarguably, DL- models demand financial and scientific investments, primarily available to economically prosperous countries.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> Additionally, low- and middle-income countries' (LMICs) scarcity of infrastructure generates additional challenges, mainly concerning their ability to inform patients, communicate uncertainty, administer consent, and generate robust data.<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">6</span></a> Consequently, these nations are forced to use DL- models trained on data from developed countries, which are different from the reality of rural areas.<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">10</span></a> This scenario aggravates social inequalities regarding healthcare and highlights the use limitations of DL- algorithms in the regions that would benefit the most, such as those with insufficient resources, specialists, and technology.</p><p id="par0110" class="elsevierStylePara elsevierViewall">Moreover, the lack of analyzed data promotes harm to underrepresented groups based on their gender, sexual orientation, ethnicity, comorbidities, social status, or economic factors, among others.<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1–3,5,7,8</span></a><span class="elsevierStyleSup">,</span><a class="elsevierStyleCrossRef" href="#bib0055"><span class="elsevierStyleSup">11</span></a> Analytical healthcare studies demonstrate significant differences in underdiagnosis rates depending on the above-mentioned variables.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> For instance, if training datasets fail to present "rare" conditions, AI algorithms will fail to identify structures resembling inherent traits from underrepresented groups.<a class="elsevierStyleCrossRefs" href="#bib0040"><span class="elsevierStyleSup">8,11</span></a> Therefore, all potential sources of bias must be considered to reduce their impact on AI's decisions.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a></p><p id="par0115" class="elsevierStylePara elsevierViewall">Envisioning a solution for sampling bias, the external validation method guarantees the generalization of AI systems by using representative data from other institutions. Unfortunately, despite its relevance, only 6% of recent medical DL papers included validation on independent external data.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p></span><span id="sec0030" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0055">Reporting guidelines</span><p id="par0120" class="elsevierStylePara elsevierViewall">Some ethical concerns included safety, transparency, and value alignment.<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1,3,7</span></a> AI systems must be verifiable and secure to ensure safety.<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3,7,12</span></a> Although the algorithm's actions may be discernible, understanding its decision-making rationale may be challenging, underscoring the importance of transparency.<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3,7,8,12</span></a> Value alignment is about optimizing AI's work for the patient's benefit, bearing this responsibility to researchers and radiologists.<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3,7</span></a></p><p id="par0125" class="elsevierStylePara elsevierViewall">New analysis procedures tailored to the nature of algorithms are needed in addition to standard root-cause analysis.<a class="elsevierStyleCrossRefs" href="#bib0015"><span class="elsevierStyleSup">3,7,12</span></a> In addition, the expanding literature on AI in medical imaging requires transparent and systematic reporting of research.<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a></p><p id="par0130" class="elsevierStylePara elsevierViewall">FUTURE-AI (Fairness Universality Traceability Usability Robustness Explainability-AI), published in 2021, proposed broad principles for AI development in medical imaging, encompassing research, design, and deployment. Unlike prior guidelines, which focused on manuscript structure, FUTURE-AI emphasizes AI systems' fairness, usability, robustness, and explainability.</p><p id="par0135" class="elsevierStylePara elsevierViewall">It introduces novel topics, including "clinical conception," "end-user requirement gathering," and "AI deployment and monitoring," aiming for equitable and minimally biased systems.<a class="elsevierStyleCrossRef" href="#bib0065"><span class="elsevierStyleSup">13</span></a></p><p id="par0140" class="elsevierStylePara elsevierViewall">The DECIDE-AI (Developmental and Exploratory Clinical Investigations of Decision support systems driven by AI ) guideline, published in 2022, aims to ensure transparent reporting of clinical studies evaluating AI systems and address human influence on clinical AI performance. This framework enabled and uniformed the assessment of complex interventions by approaching a stage of development, the early clinical trials, instead of a type of study. It provides a checklist for live evaluation to analyze clinical utility and safety, assess users' learning curves, and prepare the algorithm for larger-scale evaluations.<a class="elsevierStyleCrossRef" href="#bib0070"><span class="elsevierStyleSup">14</span></a></p><p id="par0145" class="elsevierStylePara elsevierViewall">Currently, the AI - guidelines under development include STARD-AI and TRIPOD-AI. The first standardize diagnostic accuracy reports, while the latter evaluates prediction model studies.<a class="elsevierStyleCrossRefs" href="#bib0065"><span class="elsevierStyleSup">13,14</span></a></p></span><span id="sec0035" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0060">Technical validation</span><p id="par0150" class="elsevierStylePara elsevierViewall">Many AI systems for high-stakes decisions are developed daily, risking adverse outcomes.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a> For AI's radiology performance to be safe and effective, validation criteria such as robustness, reproducibility, and generalizability must be met. Unfortunately, designing a technical validation study is challenging since most studies use the same dataset for algorithm development, optimization, and validation. It leads to a need for more generalizability and robustness verification, besides possible data leakage. It is essential to specify the expected accuracy of the algorithm before proceeding to the next verification steps. Technical approval must be followed by real clinical validation to ensure patient safety. The algorithm's performance must be evaluated before implementing it in a routine setting.<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">9</span></a></p><p id="par0155" class="elsevierStylePara elsevierViewall">The implementation of DL-powered systems must ensure safety, efficacy, and equity. However, the current form of DL explainability techniques could be more suitable for fields in which patients' lives are at stake, such as radiology.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p></span><span id="sec0040" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0065">Regulatory frameworks</span><p id="par0160" class="elsevierStylePara elsevierViewall">In deploying DL systems, ethical standards are ensured through different methods in each country. Europe requires the CE mark, while the USA mandates clearance from regulatory agencies like the FDA and local IRBs.<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">8</span></a></p><p id="par0165" class="elsevierStylePara elsevierViewall">Approval from regulatory frameworks is crucial for adopting AI systems in medical practice. However, the current protocols have limitations and need improvement to evaluate diagnostic AI algorithms comprehensively.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p><p id="par0170" class="elsevierStylePara elsevierViewall">The International Medical Device Regulators Forum (IMDRF) sets the standards followed by most medical software regulators, such as the FDA and the frameworks from the European Union (EU). The regulatory bodies broadly explore AI systems' safety, effectiveness, and performance.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> However, they stumble on crucial points such as the conflation of the diagnostic task and diagnostic algorithm, simple definition of the diagnostic task, absence of mechanisms to compare similar algorithms directly, poor definition of safety and performance elements, and lack of resources to access performance at each installed site.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p><p id="par0175" class="elsevierStylePara elsevierViewall">A discrete evaluation process enhances the ability to address software issues and allows for comparison. The process involves defining the diagnostic task, testing in a controlled environment, evaluating real-world effectiveness, assessing durability over time, and setting internal developer benchmarks. Guidelines based on this approach improve control over the longitudinal implantation of AI systems.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p><p id="par0180" class="elsevierStylePara elsevierViewall">Conventional regulatory frameworks must ensure excellence at every medical software application site.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a> To address this, third-party evaluators, such as clinical research</p><p id="par0185" class="elsevierStylePara elsevierViewall">organizations, research laboratories, or organizations that develop and maintain reference standard data sets, could be utilized. This approach is already implemented in drug studies under the supervision of regulatory agencies.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p></span><span id="sec0045" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0070">Conflict of interest</span><p id="par0190" class="elsevierStylePara elsevierViewall">In nascent radiology AI markets, radiologists involved in patient care may also have positions in AI startups or established commercial entities.<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1–3</span></a> Similar to drug investigators with financial interests in drug success, COI related to AI products may be managed through remedies like public disclosure, divestment, or oversight. Medical software manufacturers funding and publishing evaluations of their products may also create COI.<a class="elsevierStyleCrossRef" href="#bib0075"><span class="elsevierStyleSup">15</span></a></p><p id="par0195" class="elsevierStylePara elsevierViewall">Stakeholders responsible for sharing patient data, procuring AI agents, or implementing models in clinical workflows should carefully manage their conflicts of interest when dealing with AI in healthcare. In some cases, they may need to recuse themselves from such activities.<a class="elsevierStyleCrossRef" href="#bib0015"><span class="elsevierStyleSup">3</span></a></p></span><span id="sec0050" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0075">The emergence of AI tools for text production</span><p id="par0200" class="elsevierStylePara elsevierViewall">We must consider that, with the emergence of AI tools for text production, this becomes more worrying to be considered related to research.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a> For example, Large Language Models (LLMs) qualified by AI can generate increasingly complex phrases to distinguish from the text written by people. At the same time, ChatGPT and Oher LLMS increase repair in education and scientific audiences due to their ability to write text for evaders, reports, examinations, and scientific papers.<a class="elsevierStyleCrossRef" href="#bib0080"><span class="elsevierStyleSup">16</span></a></p></span><span id="sec0055" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0080">Conclusion</span><p id="par0205" class="elsevierStylePara elsevierViewall">In the Big Data era, the seven research ethics requirements proposed by Emanuel, E. J. (2000) must be revised for AI-related practice and research. Therefore, frequent discussions and debates are necessary.<a class="elsevierStyleCrossRefs" href="#bib0005"><span class="elsevierStyleSup">1,3</span></a> In conclusion, the incorporation of AI into radiology has enhanced the efficiency and precision of radiologists. However, resolving ethical concerns surrounding patient data, algorithms, and conflicts of interest is essential to ensure patient safety and privacy. Developing and enforcing regulatory frameworks and ethical principles can assist in mitigating potential moral concerns and maximizing the benefits of AI in radiology.</p></span><span id="sec0061" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0081">Study information</span><p id="par0211" class="elsevierStylePara elsevierViewall">This study was performed at the Neuroradiology Department of Hospital Antonio Prudente, Fortaleza - CE, Brazil.</p></span><span id="sec0060" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0085">Funding source</span><p id="par0210" class="elsevierStylePara elsevierViewall">This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:18 [ 0 => array:3 [ "identificador" => "xres1990958" "titulo" => "Abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0005" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1708806" "titulo" => "Keywords" ] 2 => array:3 [ "identificador" => "xres1990959" "titulo" => "Resumen" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abst0010" ] ] ] 3 => array:2 [ "identificador" => "xpalclavsec1708807" "titulo" => "Palabras clave" ] 4 => array:2 [ "identificador" => "sec0005" "titulo" => "Introduction" ] 5 => array:2 [ "identificador" => "sec0010" "titulo" => "The ethics of data" ] 6 => array:2 [ "identificador" => "sec0015" "titulo" => "Data collection and management" ] 7 => array:2 [ "identificador" => "sec0020" "titulo" => "The ethics of algorithms" ] 8 => array:2 [ "identificador" => "sec0025" "titulo" => "The ethics of practice" ] 9 => array:2 [ "identificador" => "sec0030" "titulo" => "Reporting guidelines" ] 10 => array:2 [ "identificador" => "sec0035" "titulo" => "Technical validation" ] 11 => array:2 [ "identificador" => "sec0040" "titulo" => "Regulatory frameworks" ] 12 => array:2 [ "identificador" => "sec0045" "titulo" => "Conflict of interest" ] 13 => array:2 [ "identificador" => "sec0050" "titulo" => "The emergence of AI tools for text production" ] 14 => array:2 [ "identificador" => "sec0055" "titulo" => "Conclusion" ] 15 => array:2 [ "identificador" => "sec0061" "titulo" => "Study information" ] 16 => array:2 [ "identificador" => "sec0060" "titulo" => "Funding source" ] 17 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-03-29" "fechaAceptado" => "2023-05-21" "PalabrasClave" => array:2 [ "en" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1708806" "palabras" => array:5 [ 0 => "Artificial intelligence" 1 => "Radiology" 2 => "Ethics" 3 => "Algorithms bias" 4 => "Machine learning" ] ] ] "es" => array:1 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Palabras clave" "identificador" => "xpalclavsec1708807" "palabras" => array:5 [ 0 => "Inteligencia artificial" 1 => "Radiología" 2 => "Ética" 3 => "Sesgo algorítmico" 4 => "Aprendizaje automático" ] ] ] ] "tieneResumen" => true "resumen" => array:2 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span id="abst0005" class="elsevierStyleSection elsevierViewall"><p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">The analysis of ethical aspects in clinical research has always been a challenge and has required constant updates.</p><p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">In short, research ethics is the set of specific principles, rules, and norms of behavior that a research community has decided are appropriate and fair under the premise that research must be valid, reliable, legitimate, and representative.</p><p id="spar0015" class="elsevierStyleSimplePara elsevierViewall">This non-systematic review brings some ethical concerns that should be considered within the scientific community. Many studies and the development of new artificial intelligence (AI) tools, especially in radiology, make it necessary for the radiology research community to promote debates and establish ethical standards for the practice and development of new AI tools.</p></span>" ] "es" => array:2 [ "titulo" => "Resumen" "resumen" => "<span id="abst0010" class="elsevierStyleSection elsevierViewall"><p id="spar0020" class="elsevierStyleSimplePara elsevierViewall">El análisis de aspectos éticos en la investigación clínica siempre ha supuesto un reto que ha requerido de constantes actualizaciones.</p><p id="spar0025" class="elsevierStyleSimplePara elsevierViewall">En resumen, la ética en la investigación es el conjunto de principios, reglas y normas específicas del comportamiento que una comunidad investigadora ha decidido como apropiadas y justas bajo la premisa de que la investigación debe caracterizarse por ser válida, fiable, legítima y representativa.</p><p id="spar0030" class="elsevierStyleSimplePara elsevierViewall">Se presenta una revisión no sistemática que trata algunas de las preocupaciones éticas que la comunidad científica debe tener en cuenta. Numerosos estudios sobre el desarrollo de nuevas herramientas de inteligencia artificial (IA), especialmente aplicadas a la radiología, hacen necesario que la comunidad investigadora en esta área promueva debates y establezca unos principios éticos aplicables a la práctica y al desarrollo de las nuevas herramientas de IA.</p></span>" ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:16 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "What makes clinical research ethical?" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:1 [ 0 => "E.J. 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