Ethical Data Collection for Medical Image Analysis: a Structured Approach

Padmapriya, S.; Parthasarathy, S.

doi:10.1007/s41649-023-00250-9

Cited by 12 publications

(4 citation statements)

References 32 publications

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“…The study by Padmapriya andParthasarathy (2021), Facca et al (2020) and Stainton and Iordanova (2017) provide diverse aspects of these ethical considerations, ranging from healthcare research to digital data collection involving minors and the analysis of online content. These developments reflect a broader trend towards responsible and transparent data handling, ensuring that the benefits of data science are realized without compromising ethical principles.…”

Section: Results Of the Study Current Ethical Practices In Data Colle...mentioning

confidence: 99%

Ethical Considerations in Data Collection and Analysis: A Review: Investigating Ethical Practices and Challenges in Modern Data Collection and Analysis

Gold Nmesoma Okorie,

Chioma Ann Udeh,

Ejuma Martha Adaga

et al. 2024

Int. j. appl. res. soc. sci.

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In an era where data profoundly influences decision-making across various sectors, this comprehensive review critically examines the evolving landscape of data science ethics, particularly focusing on the interplay between technological advancements and ethical standards. The study aims to investigate and synthesize current ethical practices and challenges in modern data collection and analysis, tracing the evolution of ethical standards in data science, understanding the significance of ethical considerations in contemporary data practices, and exploring the development of global regulatory and ethical frameworks. The paper encompasses a systematic literature review, focusing on core ethical principles in data collection and analytical processes, the roles of consent and privacy, the complexities introduced by big data, and the intricacies of ethical frameworks across different regions. It bridges the research gap in data ethics by providing insights into practical ethical frameworks and instructional models, guiding researchers, policymakers, and practitioners in ethical data handling. The study concludes that ethical considerations are integral to data science practices and contribute significantly to societal well-being. It recommends enhanced ethical education in data science, development of inclusive ethical frameworks, strengthening regulatory oversight, and promoting public engagement in data ethics discussions. These steps are essential for ensuring responsible and beneficial use of data in an ethically complex landscape. Keywords: Data Ethics, Data Science, Ethical Standards, Privacy, Consent, Regulatory.

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Section: Results Of the Study Current Ethical Practices In Data Colle...mentioning

confidence: 99%

Ethical Considerations in Data Collection and Analysis: A Review: Investigating Ethical Practices and Challenges in Modern Data Collection and Analysis

Gold Nmesoma Okorie,

Chioma Ann Udeh,

Ejuma Martha Adaga

et al. 2024

Int. j. appl. res. soc. sci.

View full text Add to dashboard Cite

show abstract

“…Ethical guidelines require informed consent from patients before collecting and using their data for AI-driven diagnostics. It is essential to balance anonymizing data to protect patient identities and retaining clinical relevance to ensure ethical use of patient information [ 39 ].…”

Section: Reviewmentioning

confidence: 99%

Applications of Artificial Intelligence in Microbial Diagnosis

Shelke,

Badge,

Bankar

2023

Cureus

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The diagnosis is an important factor in healthcare care, and it is essential to identify microorganisms that cause infections and diseases. The application of artificial intelligence (AI) systems can improve disease management, drug development, antibiotic resistance prediction, and epidemiological monitoring in the field of microbial diagnosis. AI systems can quickly and accurately detect infections, including new and drug-resistant strains, and enable early detection of antibiotic resistance and improved diagnostic techniques. The application of AI in bacterial diagnosis focuses on the speed, precision, and identification of pathogens and the ability to predict antibiotic resistance.

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“…In recent times, deep learning emerged as a highly efficient and cost-effective approach for ameliorating kidney disease diagnosis via image processing and classification. Collecting medical images, nevertheless, poses significant challenges for scientists [11]. Ethical issues such as safeguarding patients' privacy, ensuring anonymity, and obtaining informed consent are closely associated with the data-gathering process [11].…”

Section: Introductionmentioning

confidence: 99%

“…Collecting medical images, nevertheless, poses significant challenges for scientists [11]. Ethical issues such as safeguarding patients' privacy, ensuring anonymity, and obtaining informed consent are closely associated with the data-gathering process [11]. Various laws and regulations are set to ensure the ethical standards are compiled during all process of data collection.…”

Section: Introductionmentioning

confidence: 99%

The investigation of transferability utilizing the ImageNet weight-based pretrained model for medical image classification: A case study on kidney CT images

Hsu

2023

ACE

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Due to recent growth in technology, machine learning has emerged to be an effective auxiliary tool in medical field. However, the effectiveness of transfer learning architectures trained on non-medical image data remains unclear. In this paper, two VGG-16 models, a type of pre-trained Convolutional Neural Network architecture, were constructed to classify kidney CT images that belong to four categories: normal, stone, cyst, and tumor. Two VGG-16 models have identical parameters except for the pre-trained weights: one has pre-trained weights trained on ImageNet, and the other one trained on a random large-scale dataset. To gather a more detained insight into models performances, saliency maps and Grad-CAM are employed to assess the models' ability to extract relevant features from the CT images. The result demonstrated that VGG-16 model that is trained on ImageNet can achieve 98.96% accuracy, which is about 30% higher than the other VGG-16 model. The saliency maps and Grad-CAM also support the difference in test accuracy: the model with random pre-trained dataset has saliency map that highlights the whole picture and Grad-CAM image that does not highlight any part of the CT image data, showing that it cannot correctly locate the key features. Additionally, the model with ImageNet can correctly highlight the principal features in both maps. In this study, the utilization of ImageNet is proven to be effective in the usage of transfer learning in processing medical image. Future research and exploration should focus on further enhancing the application of transfer learning in the medical field.

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Ethical Data Collection for Medical Image Analysis: a Structured Approach

Cited by 12 publications

References 32 publications

Ethical Considerations in Data Collection and Analysis: A Review: Investigating Ethical Practices and Challenges in Modern Data Collection and Analysis

Ethical Considerations in Data Collection and Analysis: A Review: Investigating Ethical Practices and Challenges in Modern Data Collection and Analysis

Applications of Artificial Intelligence in Microbial Diagnosis

The investigation of transferability utilizing the ImageNet weight-based pretrained model for medical image classification: A case study on kidney CT images

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