Artificial Intelligence in Medical Diagnoses and the Right to Explanation

Hoeren, Thomas; Niehoff, Maurice

doi:10.21552/edpl/2018/3/9

Cited by 12 publications

(7 citation statements)

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“…For instance, in relation to AI-based recommendations and decisions made by automated vehicle technologies responding to unavoidable road traffic accidents (Cunneen et al, 2019 ). Concerns have emerged about the use of AI-based recommendations related to the accuracy of medical diagnosis and prognosis (Jain et al, 2020 ; Thrall et al, 2021 ), how inaccurate AI-based healthcare recommendations may adversely impact levels of trust between physicians and patients (Hoeren & Niehoff, 2018 ), as well as new technology acceptance levels among users (Fan et al, 2018 ). While technology adoption and acceptance issues are brought up as potential adverse consequences of questioning AI-based recommendations, but the discussion of how espoused national cultural values might affect levels of healthcare IS artifact adoption or acceptance is outside the scope of this study.…”

Section: Literature Reviewmentioning

confidence: 99%

Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

2021

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One realm of AI, recommender systems have attracted significant research attention due to concerns about its devastating effects to society’s most vulnerable and marginalised communities. Both media press and academic literature provide compelling evidence that AI-based recommendations help to perpetuate and exacerbate racial and gender biases. Yet, there is limited knowledge about the extent to which individuals might question AI-based recommendations when perceived as biased. To address this gap in knowledge, we investigate the effects of espoused national cultural values on AI questionability, by examining how individuals might question AI-based recommendations due to perceived racial or gender bias. Data collected from 387 survey respondents in the United States indicate that individuals with espoused national cultural values associated to collectivism, masculinity and uncertainty avoidance are more likely to question biased AI-based recommendations. This study advances understanding of how cultural values affect AI questionability due to perceived bias and it contributes to current academic discourse about the need to hold AI accountable.

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Section: Literature Reviewmentioning

confidence: 99%

Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

2021

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“…Artificial intelligence and machine learning applications are complex and known for their black-box nature, providing predictions without enough explanation [ 52 ]. Besides the accurate prediction and the decrease in workload, trust in algorithmic decisions is essential, especially in medicine and health care research [ 53 ]. To overcome the lack of transparency, interpreting machine learning models and their decision-making process has become a growing focus among academic and industrial machine learning experts [ 54 ].…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation

Abdelkader¹,

Navarro²,

Parrish³

et al. 2021

JMIR Res Protoc

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Background A barrier to practicing evidence-based medicine is the rapidly increasing body of biomedical literature. Use of method terms to limit the search can help reduce the burden of screening articles for clinical relevance; however, such terms are limited by their partial dependence on indexing terms and usually produce low precision, especially when high sensitivity is required. Machine learning has been applied to the identification of high-quality literature with the potential to achieve high precision without sacrificing sensitivity. The use of artificial intelligence has shown promise to improve the efficiency of identifying sound evidence. Objective The primary objective of this research is to derive and validate deep learning machine models using iterations of Bidirectional Encoder Representations from Transformers (BERT) to retrieve high-quality, high-relevance evidence for clinical consideration from the biomedical literature. Methods Using the HuggingFace Transformers library, we will experiment with variations of BERT models, including BERT, BioBERT, BlueBERT, and PubMedBERT, to determine which have the best performance in article identification based on quality criteria. Our experiments will utilize a large data set of over 150,000 PubMed citations from 2012 to 2020 that have been manually labeled based on their methodological rigor for clinical use. We will evaluate and report on the performance of the classifiers in categorizing articles based on their likelihood of meeting quality criteria. We will report fine-tuning hyperparameters for each model, as well as their performance metrics, including recall (sensitivity), specificity, precision, accuracy, F-score, the number of articles that need to be read before finding one that is positive (meets criteria), and classification probability scores. Results Initial model development is underway, with further development planned for early 2022. Performance testing is expected to star in February 2022. Results will be published in 2022. Conclusions The experiments will aim to improve the precision of retrieving high-quality articles by applying a machine learning classifier to PubMed searching. International Registered Report Identifier (IRRID) DERR1-10.2196/29398

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“…Such balance is generally guaranteed by the data protection legislation such as Health Insurance Portability and Accountability Act 1996 (HIPAA) of the USA and General Data Protection Regulation 2016 (GDPR) of the European Union (EU). The effectiveness of these legislation in the environment of big data analytics is still debatable ( Casey et al., 2019 ; Gil González and de Hert, 2019 ; Hoeren and Niehoff, 2018 ; Kesa and Kerikmae, 2020 ; McGraw and Mandl, 2021 ; Turpin et al., 2020 ). Despite these concerns, artificial intelligence technologies have already shown outstanding results in medicine and have expected to bring further innovations into medical technologies and future healthcare ( Park et al., 2020 ).…”

Section: Related Workmentioning

confidence: 99%

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

Hassan

Dhali

Zaman

et al. 2021

Heliyon

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Big data analytics and artificial intelligence are revolutionizing the global healthcare industry. As the world accumulates unfathomable volumes of data and health technology grows more and more critical to the advancement of medicine, policymakers and regulators are faced with tough challenges around data security and data privacy. This paper reviews existing regulatory frameworks for artificial intelligence-based medical devices and health data privacy in Bangladesh. The study is legal research employing a comparative approach where data is collected from primary and secondary legal materials and filtered based on policies relating to medical data privacy and medical device regulation of Bangladesh. Such policies are then compared with benchmark policies of the European Union and the USA to test the adequacy of the present regulatory framework of Bangladesh and identify the gaps in the current regulation. The study highlights the gaps in policy and regulation in Bangladesh that are hampering the widespread adoption of big data analytics and artificial intelligence in the industry. Despite the vast benefits that big data would bring to Bangladesh's healthcare industry, it lacks the proper data governance and legal framework necessary to gain consumer trust and move forward. Policymakers and regulators must work collaboratively with clinicians, patients and industry to adopt a new regulatory framework that harnesses the potential of big data but ensures adequate privacy and security of personal data. The article opens valuable insight to regulators, academicians, researchers and legal practitioners regarding the present regulatory loopholes in Bangladesh involving exploiting the promise of big data in the medical field. The study concludes with the recommendation for future research into the area of privacy as it relates to artificial intelligence-based medical devices should consult the patients' perspective by employing quantitative analysis research methodology.

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Artificial Intelligence in Medical Diagnoses and the Right to Explanation

Cited by 12 publications

References 0 publications

Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

Questioning Racial and Gender Bias in AI-based Recommendations: Do Espoused National Cultural Values Matter?

A Deep Learning Approach to Refine the Identification of High-Quality Clinical Research Articles From the Biomedical Literature: Protocol for Algorithm Development and Validation

Big data and predictive analytics in healthcare in Bangladesh: regulatory challenges

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