What Are Important Ethical Implications of Using Facial Recognition Technology in Health Care?

Martinez-Martin, Nicole

doi:10.1001/amajethics.2019.180

Cited by 52 publications

(32 citation statements)

References 16 publications

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“…The increasing use and quality of facial phenotyping software in clinical genetics should also be accompanied by an ethical evaluation of these systems [ 45 ]. This affects issues such as the automation of medical diagnostic action, the sharing of (potentially identifiable) data, and a potentially altered doctor-patient relationship.…”

Section: Discussionmentioning

confidence: 99%

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Pantel¹,

Hajjir²,

Danyel³

et al. 2020

J Med Internet Res

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Background Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.

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Section: Discussionmentioning

confidence: 99%

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Pantel¹,

Hajjir²,

Danyel³

et al. 2020

J Med Internet Res

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“…Finally, AI will certainly be applied more in the health care system in the future owing to its advantages over the traditional decision-making process. On the other hand, the fact that users do not know how the results are analyzed by the “black box” algorithms, ethnic differences in validity of facial recognition technology for genetic diagnosis, medical and behavioral conditions [51,52], and ethnic bias in training data set [53] raise questions about product liability, privacy and data protection, and ethical and legal issues [51]. Thus, researchers have voiced their concern about legacy and ethical guidelines that are lagging behind the development of AI in health care and medicine [51].…”

Section: Discussionmentioning

confidence: 99%

Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study

et al. 2019

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BackgroundArtificial intelligence (AI)–based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field.ObjectiveThis study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018.MethodsWe obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape.ResultsThe applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources.ConclusionsThe application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

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“…Martinez-Martin stated that we will need to be careful and study of the broader impact of facial recognition technologies (FRT) in health care settings, including issues as liability [ 32 ]. The author provided the example that if FRT diagnostic software develops to the point that it is used not just to augment but to replace a physician’s judgment, ethical and legal questions may arise regarding which entity appropriately has liability [ 32 ]. Indeed it is of utmost important to assess the relative benefits and burdens of specific FRT uses in health care [ 32 ].…”

Section: Why An Early Diagnosis Is Important: the Clinician’s Poinmentioning

confidence: 99%

“…The author provided the example that if FRT diagnostic software develops to the point that it is used not just to augment but to replace a physician’s judgment, ethical and legal questions may arise regarding which entity appropriately has liability [ 32 ]. Indeed it is of utmost important to assess the relative benefits and burdens of specific FRT uses in health care [ 32 ]. Therefore, privacy and data protections are key to advancing FRT and making it helpful [ 32 ].…”

Section: Why An Early Diagnosis Is Important: the Clinician’s Poinmentioning

confidence: 99%

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Towards an Earlier Diagnosis of Acromegaly and Gigantism

Sisco¹,

Lely

2021

JCM

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Acromegaly is a rare disease and the clinical features of acromegaly develop insidiously; its diagnosis is often significantly delayed. Therefore, earlier diagnosis will improve the quality of life of the patient and reduce the need for other therapies to control the initial and ongoing damage that acromegaly presents. In this chapter, we describe the view of the patient and the clinician on the importance of earlier diagnosis, as well as on what can be done to speed up this process. Earlier diagnosis will not only improve quality of life and the burden of disease in acromegaly patients, but it will also have a positive impact in the economic burden of this rare disease.

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What Are Important Ethical Implications of Using Facial Recognition Technology in Health Care?

Cited by 52 publications

References 16 publications

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study

Towards an Earlier Diagnosis of Acromegaly and Gigantism

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