Artificial intelligence in medical devices and clinical decision support systems

Sloane, Elliot B.; Silva, Ricardo J.

doi:10.1016/b978-0-12-813467-2.00084-5

Cited by 55 publications

(30 citation statements)

References 15 publications

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“…6 The rules that the CDSS follows to reason about a specific patient are either programmed by the developers (ie, 'knowledge' or 'rule-based' expert systems), or inferred from a large amount of data about a group of patients, using statistical AI methods, such as machine learning or deep learning algorithms (ie, 'data-driven'). [8][9][10] Preventing risks of CDSS by better understanding cognitive tasks.…”

Section: Applications Of Cdssmentioning

confidence: 99%

“…10 As such, a knowledge-based system can best be thought of as a database of 'best-practice' rules that can be employed to find the most suitable procedure (eg, examination or treatment) for an individual patient. 9 The 'logic' employed by the system can be represented as formal rules, such as 'when a patient with disease X also has symptom Y, use medication Z'. As such, the 'reasoning' employed by the system to arrive at a specific advice can easily be backtracked and evaluated.…”

Section: Clinical Decision Support Systemsmentioning

confidence: 99%

“…10 The 'logic' employed by this type of CDSS is (rather than rule-following as in knowledge-based CDSS), based on comparisons between cases, such as 'other patients with disease X and symptom Y have benefited from using medication Z'. 9 Because data-driven CDSSs are often trained using data from thousands of cases or more, a multitude of the amount of cases that a physician sees in a lifetime, these systems are able to detect very subtle and complex patterns in the data (eg, Savage 2020 12 ). However, unlike knowledge-based AI, the decision made in a data-driven CDSS cannot easily be explained, 6 which leads to critical questions about the robustness, explainability, reliability and accountability of these types of systems.…”

Section: Clinical Decision Support Systemsmentioning

confidence: 99%

“…This can lead to unacceptable bias. 9 Therefore, they state that 'When an AI system makes a decision, humans affected by these decisions should be able to get an explanation why the decision is made in terms of language they can understand and they should be able to challenge the decision with reasoned arguments' (ibid, 489). 5 These requirements for the use of AI systems are indicated by the developers of machine learning developers by the concept of explainable AI.…”

Section: Explainable and Accountable Crss To Facilitate Interaction With The Clinicianmentioning

confidence: 99%

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From clinical decision support to clinical reasoning support systems

Baalen

Boon

Verhoef

2021

Evaluation Clinical Practice

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Despite the great promises that artificial intelligence (AI) holds for health care, the uptake of such technologies into medical practice is slow. In this paper, we focus on the epistemological issues arising from the development and implementation of a class of AI for clinical practice, namely clinical decision support systems (CDSS). We will first provide an overview of the epistemic tasks of medical professionals, and then analyse which of these tasks can be supported by CDSS, while also explaining why some of them should remain the territory of human experts. Clinical decision making involves a reasoning process in which clinicians combine different types of information into a coherent and adequate 'picture of the patient' that enables them to draw explainable and justifiable conclusions for which they bear epistemological responsibility. Therefore, we suggest that it is more appropriate to think of a CDSS as clinical reasoning support systems (CRSS). Developing CRSS that support clinicians' reasoning process therefore requires that: (a) CRSSs are developed on the basis of relevant and well-processed data; and (b) the system facilitates an interaction with the clinician. Therefore, medical experts must collaborate closely with AI experts developing the CRSS. In addition, responsible use of an CRSS requires that the data generated by the CRSS is empirically justified through an empirical link with the individual patient. In practice, this means that the system indicates what factors contributed to arriving at an advice, allowing the user (clinician) to evaluate whether these factors are medically plausible and applicable to the patient. Finally, we defend that proper implementation of CRSS allows combining human and artificial intelligence into hybrid intelligence, were both perform clearly delineated and complementary empirical tasks. Whereas CRSSs can assist with statistical reasoning and finding patterns in complex data, it is the clinicians' task to interpret, integrate and contextualize.

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Section: Applications Of Cdssmentioning

confidence: 99%

Section: Clinical Decision Support Systemsmentioning

confidence: 99%

Section: Clinical Decision Support Systemsmentioning

confidence: 99%

Section: Explainable and Accountable Crss To Facilitate Interaction With The Clinicianmentioning

confidence: 99%

See 2 more Smart Citations

From clinical decision support to clinical reasoning support systems

Baalen

Boon

Verhoef

2021

Evaluation Clinical Practice

View full text Add to dashboard Cite

show abstract

“…Second, AI can be used to group patients based on electronic health record information and optimize them for individual institutions [ 14 ]. Third, because of the complexity of human physiology and the diversity of human diseases, AI will not make absolute decisions [ 15 ]. Experienced clinicians who frequently use a System 1 diagnostic process may use the completeness of the differential diagnosis by AI to avoid pitfalls [ 13 ].…”

Section: Perspectivementioning

confidence: 99%

A Perspective from a Case Conference on Comparing the Diagnostic Process: Human Diagnostic Thinking vs. Artificial Intelligence (AI) Decision Support Tools

Harada

Shimizu

Kaji

et al. 2020

IJERPH

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Artificial intelligence (AI) has made great contributions to the healthcare industry. However, its effect on medical diagnosis has not been well explored. Here, we examined a trial comparing the thinking process between a computer and a master in diagnosis at a clinical conference in Japan, with a focus on general diagnosis. Consequently, not only was AI unable to exhibit its thinking process, it also failed to include the final diagnosis. The following issues were highlighted: (1) input information to AI could not be weighted in order of importance for diagnosis; (2) AI could not deal with comorbidities (see Hickam’s dictum); (3) AI was unable to consider the timeline of the illness (depending on the tool); (4) AI was unable to consider patient context; (5) AI could not obtain input information by themselves. This comparison of the thinking process uncovered a future perspective on the use of diagnostic support tools.

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