How Scientists Are Brought Back into Science—The Error of Empiricism

Boon, Mieke

doi:10.1007/978-3-030-25001-0_4

Cited by 8 publications

(6 citation statements)

References 38 publications

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“…In this way, MLA have a pragmatic value. However, the development of modeling practices must allow theorizing or identifying parameters that explain the phenomenon in its complexity: understanding the phenomenon as the sum of its parts as totality. ,− …”

Section: Critical Analysis and Identified Limitationsmentioning

confidence: 99%

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Fuentes-Cortés

Flores‐Tlacuahuac

Nigam

2022

Ind. Eng. Chem. Res.

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This work addresses recent developments for solving problems in process systems engineering based on machine learning algorithms. A general description of most popular supervised and unsupervised learning algorithms is presented, as well as the applications addressed in the current literature. Because of their wide usage and potential applications, support vector machines and neural networks are addressed as special cases. The approach used is fundamentally didactic. Therefore, several of the references included are recommendations for novice readers interested in entering the area of machine learning and data science. The applications were selected considering simplicity, popularity of the application, and accessibility for inexperienced readers, but with knowledge of the process systems engineering area. Finally, a critical perspective for future development and applications is provided. Epistemological issues and modeling limitations are discussed in order to analyze the real significance of data-driven strategies as well as a questioning of academic marketing in recent years.

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Section: Critical Analysis and Identified Limitationsmentioning

confidence: 99%

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Fuentes-Cortés

Flores‐Tlacuahuac

Nigam

2022

Ind. Eng. Chem. Res.

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“…According to her categorisation of epistemic tasks, machine-learning algorithms can match input data (eg, an image or a set of data points such as clinical signs and symptoms) with similar cases in their database; interpret input data as belonging to a specific category, defined by humans or by a machine-learning algorithm; diagnose a set of input data as probably belonging to a certain class and from that infer other properties of the target; structure large amounts of data to find patterns, correlations and causal relations; calculate in a way that outperforms humans; and simulate complex dynamic process. 15 In short, computers outperform humans when it comes to deductive and inductive reasoning, and are also rapidly improving at recognizing patterns and images. As such, the medical field in which CDSS has been most successful is radiology (and also other types of visual data, for example, electrocardiograms), detecting conditions such as tumours and other lesions in large amounts of imaging data in short amounts of time.…”

Section: Clinical Decision Support Systemsmentioning

confidence: 99%

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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“…Conventionally, scientists have focused on discovering scientific knowledge or developing scientific technologies (Boon, 2020; Jain et al, 2009; Venkatesh and Lipper, 2000; Youtie and Shapira, 2008) to contribute to national economic growth (Anaeto et al, 2016; Borup et al, 2006). Thus, physician-scientist training programs or research programs have been implemented by the government in each nation to foster economic growth, and are often considered important.…”

Section: Introductionmentioning

confidence: 99%

Preference for Scientist Occupation Among Medical and Science Students in South Korea

Bae

2024

Preprint

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Although becoming a scientist is generally a long and arduous process, fostering scientists is considered important for national development. To determine medical and science undergraduate students' preference for scientists, a designed preference survey was administered to 191 medical and 106 science majors, online. The chi-square test, regression, and mediation models were used. The models present significant associations between group and training programs, and between master's programs and scientist preference. Surprisingly, only 24% of the science students were interested in the Ph.D. program compared to 79.6% of the medical students. Less than 7% in both groups expressed interest in full-time scientist occupation. The study design and findings are newly reported. A negative public consensus of scientist occupation is identified, and master training program shows a strong association with the choice of scientist occupation. Due to the unstable scientists' status, a long-term follow-up is recommended afterward program completion for more effectively fostering scientists.

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How Scientists Are Brought Back into Science—The Error of Empiricism

Cited by 8 publications

References 38 publications

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective

From clinical decision support to clinical reasoning support systems

Preference for Scientist Occupation Among Medical and Science Students in South Korea

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