2019
DOI: 10.1002/hbm.24886
|View full text |Cite
|
Sign up to set email alerts
|

Your evidence? Machine learning algorithms for medical diagnosis and prediction

Abstract: Computer systems for medical diagnosis based on machine learning are not mere science fiction. Despite undisputed potential benefits, such systems may also raise problems. Two (interconnected) issues are particularly significant from an ethical point of view: The first issue is that epistemic opacity is at odds with a common desire for understanding and potentially undermines information rights. The second (related) issue concerns the assignment of responsibility in cases of failure. The core of the two issues… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
42
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
8
2

Relationship

1
9

Authors

Journals

citations
Cited by 65 publications
(44 citation statements)
references
References 43 publications
0
42
0
Order By: Relevance
“…As mentioned above, a particularly important application of machine learning in a healthcare context is (digital) diagnosis (see e.g. [10][11][12][13][14]. Machine learning models can detect patterns (precursor) of certain diseases within patient electronic healthcare records and inform clinicians of any anomalies.…”
mentioning
confidence: 99%
“…As mentioned above, a particularly important application of machine learning in a healthcare context is (digital) diagnosis (see e.g. [10][11][12][13][14]. Machine learning models can detect patterns (precursor) of certain diseases within patient electronic healthcare records and inform clinicians of any anomalies.…”
mentioning
confidence: 99%
“…In this sense, we interpret explanations as understanding support [59], and this latter one in the light of the critique by Kelp to both the "explanationist view" and the "manipulationist views" of understanding, that is in terms of supporting (human) understanding build a "wellconnected knowledge" [60]. According to this view, understanding how a system works does not only involve knowing a set of true propositions about the system behaviors (like in case of data about predictive accuracy and feature ranking for a given prediction), but also knowing how these propositions are interrelated, within a framework of sense-making.…”
Section: Implications For the Xai Research Fieldmentioning
confidence: 96%
“…Furthermore, rigid algorithm protocols and decision-making trees are subject to the consequences of the inability of AI to fully take in and interpret contextual information or delineate between relevant vs. non-relevant informational input even when employing deep machine learning (23). Contingencies are the norm in healthcare, and the human skill required to navigate and manage this offnominal, or unpredictable situations must be carefully weighed against the advantages of using AI technology (23)(24)(25)(26)(27)(28). User interface and data input methods are critical as voice recognition and interpretation is a major challenge of AI utilization (29).…”
Section: Artificial Intelligence Assisted Telemedicinementioning
confidence: 99%