Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care

Sivaraman, Venkatesh; Bukowski, Leigh A; Levin, Joel; Kahn, Jeremy M.; Perer, Adam

doi:10.1145/3544548.3581075

Cited by 35 publications

(5 citation statements)

References 65 publications

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“…The most important estimand during evaluation studies is the performance of the augmented clinical workflow as a whole, rather than the isolated system’s technical performance ( 49 ). Augmented workflows can cause unanticipated harm ( 50 ) and will only be effective if system recommendations are trusted and actioned by the end user; therefore, a thorough evaluation of safety ( 51 ), interpretability ( 52 ), and acceptability ( 53 ) is essential. The unforeseen poor performance of current sepsis decision support systems ( 54 ) emphasizes the importance of post-deployment surveillance ( 55 ), including surveillance of how system predictions are operationalized, which may change after initial trials.…”

Section: Current Approaches: Promise and Pitfallsmentioning

confidence: 99%

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Catling,

Nagendran,

Festor

et al. 2024

Critical Care Explorations

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Large randomized trials in sepsis have generally failed to find effective novel treatments. This is increasingly attributed to patient heterogeneity, including heterogeneous cardiovascular changes in septic shock. We discuss the potential for machine learning systems to personalize cardiovascular resuscitation in sepsis. While the literature is replete with proofs of concept, the technological readiness of current systems is low, with a paucity of clinical trials and proven patient benefit. Systems may be vulnerable to confounding and poor generalization to new patient populations or contemporary patterns of care. Typical electronic health records do not capture rich enough data, at sufficient temporal resolution, to produce systems that make actionable treatment suggestions. To resolve these issues, we recommend a simultaneous focus on technical challenges and removing barriers to translation. This will involve improving data quality, adopting causally grounded models, prioritizing safety assessment and integration into healthcare workflows, conducting randomized clinical trials and aligning with regulatory requirements.

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Section: Current Approaches: Promise and Pitfallsmentioning

confidence: 99%

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Catling,

Nagendran,

Festor

et al. 2024

Critical Care Explorations

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show abstract

“…Additionally, it is important to ensure that AI-based tools are designed to support rather than replace human clinicians and that AI systems do not perpetuate existing biases or increase health disparities with technology [67]. Ghosh et al, [68] examined the difficulties of using AI in healthcare and stated that AI tools need to be developed to help medical partitioners make informed decisions.…”

Section: Challengesmentioning

confidence: 99%

Artificial Intelligence and Public Health Context: What We Should Know?

Faerozh Madli,

Yuzainy Janin,

Shaierah Gulabdin

et al. 2024

ARASET

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The rapid technological advancement nowadays has a wide-ranging impact on almost all industries. Artificial intelligence (AI) is one of the technologies that has received much attention and has been used widely, including in the public health sector. In this light, medical practitioners and the public are anticipating the changes brought by AI technology in the public health sector. This study is focused on thoroughly discussing the relationship of AI in the context of public health. The discussion shows that AI is giving a lot of positive potential to the public health sector. However, despite the abundant potential and promise, AI is also not running away from today's challenges. The overall discussion of this study will provide a clear picture on the link between AI, public health link and related parties, including official medical-related agencies. In other words, this paper summarises the relationship between AI and public health, specifically the challenges and potential changes AI will bring forward.

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“…This might diminish the possibilities for patients and healthcare professionals to open up broader discussions and options for breast cancer‐related health care. On the other hand, it has been shown that predictive tools are not always immediately trusted and that the decisions are negotiated in a broader health care setting (Sivaraman et al, 2023). In any case, the biopolitical power here gets distribtued across the human and algorithmic nexus.…”

Section: Vignette: Nhs Predict Breast Cancer Prediction Toolmentioning

confidence: 99%

From ‘if‐then’ to ‘what if?’ Rethinking healthcare algorithmics with posthuman speculative ethics

2023

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This article discusses the role that algorithmic thinking and management play in health care and the kind of exclusions this might create. We argue that evidence‐based medicine relies on research and data to create pathways for patient journeys. Coupled with data‐based algorithmic prediction tools in health care, they establish what could be called health care algorithmics—a mode of management of healthcare that produces forms of algorithmic governmentality. Relying on a critical posthumanist perspective, we show how healthcare algorithmics is contingent on the way authority over bodies is produced and how predictive health care algorithms can reproduce inequalities of the worlds from which they are made, centreing possible futures on existing normativities regulated through algorithmic biopower. In contrast to that, we explore posthuman speculative ethics as a way to challenge understanding of ‘ethics’ and ‘care’ in healthcare algorithmics. We suggest some possible avenues towards working speculative ethics into health care while still being critically attentive to algorithmic modes of management and prediction in health care.

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Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care

Cited by 35 publications

References 65 publications

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Can Machine Learning Personalize Cardiovascular Therapy in Sepsis?

Artificial Intelligence and Public Health Context: What We Should Know?

From ‘if‐then’ to ‘what if?’ Rethinking healthcare algorithmics with posthuman speculative ethics

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