Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies

Ott, Tabea

doi:10.3389/fgene.2022.902960

Cited by 2 publications

(1 citation statement)

References 60 publications

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“…While individual health data are sensitive and should be safeguarded, population health data are a most important resource for research. A balance has to be found between these countervailing demands, both on a national and international basis [ 58 , 78 ]. The collection, storage, and processing of sensitive health data is the aim of the European digital GAIA-X initiative.…”

Section: Concept For Population Monitoring Of Down Syndromementioning

confidence: 99%

Population monitoring of trisomy 21: problems and approaches

2023

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Trisomy 21 (Down syndrome) is the most common autosomal aneuploidy among newborns. About 90% result from meiotic nondisjunction during oogenesis, which occurs around conception, when also the most profound epigenetic modifications take place. Thus, maternal meiosis is an error prone process with an extreme sensitivity to endogenous factors, as exemplified by maternal age. This contrasts with the missing acceptance of causal exogenous factors. The proof of an environmental agent is a great challenge, both with respect to ascertainment bias, determination of time and dosage of exposure, as well as registration of the relevant individual health data affecting the birth prevalence. Based on a few exemplary epidemiological studies the feasibility of trisomy 21 monitoring is illustrated. In the nearer future the methodical premises will be clearly improved, both due to the establishment of electronic health registers and to the introduction of non-invasive prenatal tests. Down syndrome is a sentinel phenotype, presumably also with regard to other congenital anomalies. Thus, monitoring of trisomy 21 offers new chances for risk avoidance and preventive measures, but also for basic research concerning identification of relevant genomic variants involved in chromosomal nondisjunction.

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Section: Concept For Population Monitoring Of Down Syndromementioning

confidence: 99%

Population monitoring of trisomy 21: problems and approaches

2023

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AI-produced certainties in health care: current and future challenges

Tretter,

Ott,

Dabrock

2023

AI Ethics

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Since uncertainty is a major challenge in medicine and bears the risk of causing incorrect diagnoses and harmful treatment, there are many efforts to tackle it. For some time, AI technologies have been increasingly implemented in medicine and used to reduce medical uncertainties. What initially seems desirable, however, poses challenges. We use a multimethod approach that combines philosophical inquiry, conceptual analysis, and ethical considerations to identify key challenges that arise when AI is used for medical certainty purposes. We identify several challenges. Where AI is used to reduce medical uncertainties, it is likely to result in (a) patients being stripped down to their measurable data points, and being made disambiguous. Additionally, the widespread use of AI technologies in health care bears the risk of (b) human physicians being pushed out of the medical decision-making process, and patient participation being more and more limited. Further, the successful use of AI requires extensive and invasive monitoring of patients, which raises (c) questions about surveillance as well as privacy and security issues. We outline these several challenges and show that they are immediate consequences of AI-driven security efforts. If not addressed, they could entail unfavorable consequences. We contend that diminishing medical uncertainties through AI involves a tradeoff. The advantages, including enhanced precision, personalization, and overall improvement in medicine, are accompanied by several novel challenges. This paper addresses them and gives suggestions about how to use AI for certainty purposes without causing harm to patients.

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Transparent human – (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies

Cited by 2 publications

References 60 publications

Population monitoring of trisomy 21: problems and approaches

Population monitoring of trisomy 21: problems and approaches

AI-produced certainties in health care: current and future challenges

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