Re-focusing explainability in medicine

Abstract

Using artificial intelligence to improve patient care is a cutting-edge methodology, but its implementation in clinical routine has been limited due to significant concerns about understanding its behavior. One major barrier is the explainability dilemma and how much explanation is required to use artificial intelligence safely in healthcare. A key issue is the lack of consensus on the definition of explainability by experts, regulators, and healthcare professionals, resulting in a wide variety of terminology and expectations. This paper aims to fill the gap by defining minimal explainability standards to serve the views and needs of essential stakeholders in healthcare. In that sense, we propose to define minimal explainability criteria that can support doctors’ understanding, meet patients’ needs, and fulfill legal requirements. Therefore, explainability need not to be exhaustive but sufficient for doctors and patients to comprehend the artificial intelligence models’ clinical implications and be integrated safely into clinical practice. Thus, minimally acceptable standards for explainability are context-dependent and should respond to the specific need and potential risks of each clinical scenario for a responsible and ethical implementation of artificial intelligence.

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

“…Therefore, there is an ethical obligation to inform oneself about vulnerable groups and conditions that may lead to inaccurate results in terms of accuracy, validity, uncertainty, and applicability as minimally acceptable criteria for interpretability (Arbelaez Ossa et al 2022). Furthermore, once risks have been identified, there is the obligation to inform affected patients about individual or group risks.…”

“…One may consider stratified risk levels (unacceptable, high, and low or minimal risk) as the EU's AI regulatory framework does (European Commission 2021), but the risks have to be specific. In the literature, there is the proposal to concentrate on justifiability and contestability in high-stakes situations (Henin and Le Métayer 2021) or to provide minimally acceptable criteria for explainability (Arbelaez Ossa et al 2022).…”

Machine learning, antimicrobial stewardship, and solid organ transplantation: Is this the future?

“…One concern frequently brought to light by clinicians is the fear of errors caused by blindly trusting suggested guidelines generated by algorithms they do not understand (“black box”) 2,26,48 . However, systems’ explainability need probably not to be extensive but acceptable enough for clinicians and patients to apprehend ML‐CDSS’ implications and be incorporated safely into routine practice 49 . On the one hand, concerns of the users would greatly limit the development and use of ML but on the other hand full confidence could be dangerous too.…”

Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders

“…Therefore, each decision must be logically reasoned with explainable evidence [ 49 ]. AI models might be insightful for scientists, but they should also be sufficiently clear and explainable for end users to support their decisions [ 52 ]. Otherwise, it could constitute a threat to the patient’s autonomy.…”