The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index; none of the 20 sources its claims cite is checked for support yet.
The need for uncertainty quantification in machine-assisted medical decision making
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AI snapshot
Written from the paper’s full text.
TL;DR
This perspective argues that machine-assisted medical decision making, especially deep learning, requires a principled and formal discipline of uncertainty quantification (UQ) comparable to that used in nuclear stockpile stewardship and risk management. It maps where uncertainty enters the DL pipeline, identifies four overlapping challenge areas, and sketches research directions for building UQ for DL.
Key findings
- The paper contends that predictions without UQ "are neither predictions nor actionable," and that automated clinical systems not analyzable via known causal connections will require formal UQ to be adopted, as happened in nuclear security.
- It identifies four overlapping challenge groups for UQ of data-driven approaches: (1) absence of theory (no "hard laws" in medicine), (2) absence of causal models (DL exploits correlations without causal links), (3) sensitivity to imperfect data (missing elements and errors with patterned confounds), and (4) computational expense of recomputation for uncertainty bounds.
- It proposes that uncertainty must be quantified across at least four pipeline steps: collection/selection of training data and its coverage of the medical phenomenon, accuracy and completeness of labelling, selection and understanding of the DL model and its performance bounds, and model performance against operational (clinical inference) data.
- It highlights three research directions: quantifying and limiting overfitting (noting classical information criteria fail given DL's parameter counts and capacity to memorize random noise), understanding DL internals, and training DL to provide its own uncertainty estimates—suggesting GANs for detecting out-of-sample cases as "particularly fruitful."
- Cited (not original) performance claims: DL transfer-learning applications reported performance comparable to human experts or better, and DL function estimators reported performance better than state-of-the-art classical predictive models.
Methods
- Design: a conceptual/perspective article (no empirical study, dataset, or experiment conducted by the authors).
- Content: narrative discussion of the role of DL in clinical decision making, the proposed place of UQ, anticipated challenges, and foreseen research directions, illustrated by a figure of uncertainty sources in the DL pipeline.
- Analysis: argumentative synthesis drawing analogies to UQ practice in nuclear security and risk management; no statistical analysis is performed.
Limitations
- Stated by the paper: the domains where DL is applied lack underlying mechanistic theory to ground uncertainties; DL lacks causal models, limiting conclusions; real-world data is imperfect with patterned errors that can confound prediction; and recomputation for uncertainty bounds may currently be prohibitively expensive.
- Evident in the text: the article is a position/perspective piece with no empirical validation, no concrete UQ framework or method proposed, and no quantitative results of its own; the authors explicitly note that "it is possible that the entire new field of UQ for DL might need to be developed" and that a review of ongoing research is beyond its scope.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
29 Own interpretation · 17 No basis given · 10 Not checked yet
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we are ultimately operating without the fundamental, underlying mathematical model which we could otherwise use to ground our uncertainties and to bound any assumptions we make.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4 Anticipated Challenges
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“Advancements in UQ now provide measures of confidence necessary to inform national or international security decisions.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
What the sources say
- Not checked yetcites [1] Oberkampf, W. L. & Roy, C. J. Verfication and Validation in Scientific Computing (Cambridge Univ. Press, Cambridge, 2010). source
- Not checked yetcites [2] National Research Council Evaluation of Quantification of Margins and Uncertainties: Methodology for Assessing and Certifying the Reliability of the Nuclear Stockpile (National Academies Press, Washington DC, 2009).
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The absence of a causal connection, however, means garnering limited conclusions from DL models”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2. Absence of causal models:
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“most partial theories in medicine still explain an extremely small fraction of the observed phenomena and variations 3,4 .”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2 Machine-assisted Clinical Decision Making and Research
What the sources say
- Not checked yetcites [3] Zuk, O., Hechter, E., Sunyaev, S. R. & Lander, E. S. The mystery of missing heritability: Genetic interactions create phantom heritability. Proc. Natl Acad. Sci. USA 109, 1193–1198 (2012). source
- Not checked yetcites [4] Choi, J. D. & Lee, J.-S. Interplay between epigenetics and genetics in cancer. Genomics Inform. 11, 164–173 (2013). source
plain- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“The applications of DL techniques based on transfer learning have reported performance comparable to that of the human experts 5 , , 6 or better 7 ., 8”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2.1 The Current Roles and Applications of AI
What the sources say
- Not checked yetcites [5] Kermany, D. S. et al. Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172, 1122–1131 (2018). source
- Not checked yetcites [6] Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017). source
- Not checked yetcites [7] Mar, V. & Soyer, H. Artificial intelligence for melanoma diagnosis: How can we deliver on the promise? Ann. Oncol. 29, 1625–1628 (2018). source
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“uncertainty in generalization depends on the density of training points in an appropriately defined neighborhood of the prediction target.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.3 Training DL to provide its own uncertainty estimates
plaindirect- Asserted: passes — The authors put it forward as true.
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None of the sources cited for this claim have been checked yet.
In the paper
“A particularly fruitful approach seems to be the use of Generative Adversarial Networks (GANs) for detecting out-of-sample cases.[24][25][26]”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.3 Training DL to provide its own uncertainty estimates
What the sources say
- Not checked yetcites [24] Brahma, P. P., Huang, Q. & Wu, D. O. Structured memory based deep model to detect as well as characterize novel inputs. Preprint at http://arxiv.org/abs/1801.09859 (2018).
- Not checked yetcites [25] Yu, Y., Qu, W., Li, N. & Guo, Z. Open-category classification by adversarial sample generation. Preprint at http://arxiv.org/abs/1705.08722 (2017). source
- Not checked yetcites [26] Ge, Z., Demyanov, S., Chen, Z. & Garnavi, R. Generative openmax for multi-class open set classification. Preprint at http://arxiv.org/abs/1707.07418 (2017). source
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“the capacity of DL networks to memorize random noise 18”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.1 Quantifying and limiting overfitting
What the sources say
- Not checked yetcites [18] Zhang, C., Bengio, S., Hardt, M., Recht, B. & Vinyals, O. Understanding deep learning requires rethinking generalization. Preprint at http://arxiv.org/abs/1611.03530 (2016).
plain- Asserted: passes — The authors put it forward as true.
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- Probably not a substantial claim. 44
- It may state two separate claims as one. 16
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“computing capacity in support of DL is growing exponentially”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4. Computational Expense:
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“UQ today underpins many decision processes in nuclear security, our risk management and associated investments, which can be at the scale of billions of dollars.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we propose that all of these steps (illustrated in figure 1) would need to be quantified in order to arrive at even crude, overall measures of the uncertainty of the DL-based decision model.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 3 The Role for UQ in DL
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“as DL-structured neural networks are particularly suitable for recognition of visually manifested conditions such as changes in tissue, lesions and growth, etc.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2.1 The Current Roles and Applications of AI
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Real world data is usually imperfect-typically containing missing elements and errors-and these imperfections have patterns that can confound prediction.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2. Absence of causal models:
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“the domains where the DL is usually applied, such as medicine, do not have such”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4 Anticipated Challenges
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“We see at least four overlapping groups of challenges associated with the uncertainty quantification of the data-driven approaches such as DL:”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4 Anticipated Challenges
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“To achieve this confidence, UQ extends the traditional discipline of statistical error analysis to also capture uncertainties due to possibly incomplete, inaccurate, and contradictory input data, missing and undetected mechanisms and dependencies, expert judgment, and variations between reasonable model forms and modeling strategies.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“use of scientific insights and attempts at a cohesive framework incorporating the major clinical predictors are likely to be increasingly useful as predictive models are able to efficiently summarize more complex correlations in the data.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2 Machine-assisted Clinical Decision Making and Research
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Predictions without UQ are neither predictions nor actionable.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although AI-based research has long played an important role in medicine, it has, nevertheless, been one of the more challenging areas for AI to see an effective adoption.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2 Machine-assisted Clinical Decision Making and Research
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: The reluctance to delegate to machine intelligence a function of life-critical and patient-safety-related decision making is a primary reason for the slow adoption of AI in medicine.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In most of the presented cases, the applications of AI are based on the DL neural networks, trained on a very large number of labeled data sets, and their learning tuned with the large number of hyperparameters.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2.1 The Current Roles and Applications of AI
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“A need to systematize a similar situation is what actually led us to develop the formal approach to UQ in US national security sciences.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4. Computational Expense:
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“medical AI, especially in its modern data-rich Deep Learning guise, needs to develop a principled and formal Uncertainty Quantification (UQ) discipline, just as we have seen in fields such as nuclear stockpile stewardship or risk management.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
- Rests on an assumption: The reluctance to delegate to machine intelligence a function of life-critical and patient-safety-related decision making is a primary reason for the slow adoption of AI in medicine.
None of the sources cited for this claim have been checked yet.
In the paper
“A notable example is the US support of a nuclear test moratorium since 1992, whereby we annually provide detailed measures of confidence in the safety, security, and performance of the nuclear stockpile--guaranteed through virtual testing 1,2 .”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
What the sources say
- Not checked yetcites [1] Oberkampf, W. L. & Roy, C. J. Verfication and Validation in Scientific Computing (Cambridge Univ. Press, Cambridge, 2010). source
- Not checked yetcites [2] National Research Council Evaluation of Quantification of Margins and Uncertainties: Methodology for Assessing and Certifying the Reliability of the Nuclear Stockpile (National Academies Press, Washington DC, 2009).
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The data-rich world of AI-based learning, and the frequent absence of a well-understood underlying theory poses its own unique challenges to straightforward adoption of UQ.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Obviously, there are many steps in such scenarios where there are uncertainties that would need to be quantified.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 3 The Role for UQ in DL
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“ad hoc solutions, such as sensitivity analysis and study of model variability, have sometimes been employed to mitigate some the problems we outline.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4. Computational Expense:
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“once a low-dimensional representation is arrived at describing certain correlations (e.g., the difference between cancerous and matched normal cells), it can raise hypotheses that can be tested.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2. Absence of causal models:
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These are then”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2.1 The Current Roles and Applications of AI
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This may not seem like a limitation since prediction does not need causal relation.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2. Absence of causal models:
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Understanding prediction system structure and defensibly quantifying uncertainty is possible, and, if done, can significantly benefit both research and practical applications of AI in this critical domain.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Automated systems adoption, especially systems not analyzable in terms of known causal connections, will require principled and formal UQ to play a transformative role, just as we have seen in the nuclear security domain.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2 Machine-assisted Clinical Decision Making and Research
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
In the paper
“techniques are being developed 17 to approximate some of the UQ-relevant calculations.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4. Computational Expense:
What the sources say
- Not checked yetcites [17] Gal, Y. & Ghahramani, Z. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. Preprint at https://arxiv.org/abs/1506.02142 (2015).
plain- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“But numerical results, without a measure of their veracity do not provide the trust needed to inform decisions.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The training of the DL models is computationally expensive, and any further recomputation and re-evaluation of the models, aimed, for example, at the calculation of uncertainty bounds might currently be prohibitively expensive.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4. Computational Expense:
hedgedconditional- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These challenges, while not trivial, also present significant new research opportunities for the development of new theoretical approaches, and for the practical applications of UQ in the area of machine-assisted medical decision making.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
plaindirect- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“furthermore, it is imperative to understand how the training data must be similar to prediction data.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2. Absence of causal models:
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Specific UQ methods, therefore, need to be developed to quantify the sensitivity of models to imperfect data.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2. Absence of causal models:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“DL is essentially exploiting correlations in the data, without paying attention to any causal link.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2. Absence of causal models:
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“To develop consequential decision-support from”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
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- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Unlike statistical approaches where mathematical models are used to explain variations observed in data, and to propose the margin of errors on inferences, with these recent applications, different learning architectures are combined with a large number of DL network parameters to form universal approximators.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 2.1 The Current Roles and Applications of AI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
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- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Data-driven methods are emerging as the foundations of evidence-based decision making, and the future of data-driven scientific discovery.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 6 Summary
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Some approaches, such as attempts to empirically learn generalizable patterns with insertion of random noise 19,20 , or the use of cross-validation to determine the progression of generalizable learning, move us forward in this problem space, while still carrying the problem of overfitting 21 .”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.1 Quantifying and limiting overfitting
What the sources say
- Not checked yetcites [19] Arpit, D. et al. A closer look at memorization in deep networks. Preprint at https://arxiv.org/abs/1706.05394 (2017).
- Not checked yetcites [20] Zhang, C., Vinyals, O., Munos, R. & Bengio, S. A study on overfitting in deep reinforcement learning. Preprint at http://arxiv.org/abs/1804.06893 (2018).
- Not checked yetcites [21] Cawley, G. C. & Talbot, N. L. C. On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010).
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The wider application of DL in the biomedical field now requires an extension of these methods to this emerging field.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4. Computational Expense:
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“an effective way of addressing some of the mentioned UQ problems might be to re-shape the DL engine itself to provide an uncertainty estimate on its predictions.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.3 Training DL to provide its own uncertainty estimates
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Overfitting, or the problem of a model performing well on the training set, but generalizing poorly for unseen datasets, is one of the fundamental problems of all data-centric methods, and therefore DL.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.1 Quantifying and limiting overfitting
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The details of the medical applications and DL networks are, however, significantly different since the theoretical foundations are far less developed and there are deep psychological and sociological implications in delegating to machines decisions that affect the life or health of human beings.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 6 Summary
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“progress in understanding the structure of these predictive systems, merging model and data driven approaches with strongly defensible UQ, and the formalization of the UQ for DL discipline will be needed to make DL and other data-centric tools and methods practically useful.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 6 Summary
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These have been studied in other scientific disciplines and decision-making arena, and we can learn from those.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 6 Summary
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“UQ for DL will likely not be simply a set of tools or procedures to apply, but a more complex wrap-up of disparate methods that in total help bound the overall confidence in predictions.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 6 Summary
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In high-dimensional problems, typical to the medical setting (images, large numbers of phenotypes, etc.), every point, however, can be isolated in some other dimension, and a density of points makes sense only after irrelevant dimensions, are projected out”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.3 Training DL to provide its own uncertainty estimates
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“With DL, and the large number of model parameters involved as well as the capacity of DL networks to memorize random noise 18 , classical approaches do not work.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.1 Quantifying and limiting overfitting
What the sources say
- Not checked yetcites [18] Zhang, C., Bengio, S., Hardt, M., Recht, B. & Vinyals, O. Understanding deep learning requires rethinking generalization. Preprint at http://arxiv.org/abs/1611.03530 (2016).
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Advances in understanding of how DL works internally will allow for a more effective UQ of interpreting DL.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.2 Understanding DL
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“there is a unifying structure to this problem in its various guises of complex predictive correlations in large data sets and engineering black boxes.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 6 Summary
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“there are ongoing studies that attempt to understand what DL does, 22 and how it learns 23”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.2 Understanding DL
What the sources say
- Not checked yetcites [22] Brahma, P. P., Wu, D. & She, Y. Why deep learning works: A manifold disentanglement perspective. IEEE Trans. Neural Netw. Learn. Sys. 27, 1997–2008 (2016). source
- Not checked yetcites [23] Raghu, M., Gilmer, J., Yosinski, J. & Sohl-Dickstein, J. SVCCA: Singular vector canonical correlation analysis for deep learning dynamics and interpretability. Preprint at https://arxiv.org/abs/1706.05806 (2017).
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the network itself can be used to study this uncertainty empirically and provide the uncertainty bounds.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 5.3 Training DL to provide its own uncertainty estimates
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the end, it is possible that the entire new field of UQ for DL might need to be developed.”
§ The Need for Uncertainty Quantification in Machine-assisted Medical Decision Making > 4. Computational Expense:
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper relies on this without support.
The reluctance to delegate to machine intelligence a function of life-critical and patient-safety-related decision making is a primary reason for the slow adoption of AI in medicine.
“There are many reasons for this, primarily the reluctance to delegate to machine intelligence a function of life-critical and patient-safety-related decision making.”
The authors state this as an assumption.
Fair sampling in the training data holds.
“assuming fair sampling in the training data”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Better decision-making: AI, ML, and DL can enable robots to make better decisions based on data analysis and pattern recognition, leading to improved performance and outcomes [52] .”
From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [52]
Experimental — accuracy not yet measuredIn the citing paper
“However, these models have black-box nature and lack underlying theoretical foundation behind their decisions [8]”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [8]
In the citing paper
“However, due to the unavailability of sound fundamental laws and models, the explanation of DL cannot be produced up-to the required scale.”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [8]
Citations
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MethodScalable and accurate deep learning with electronic health records.
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Optimal therapy design with tumor microenvironment normalization
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Monte Carlo Dropout for Uncertainty Analysis and ECG Trace Image Classification
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CALSeg: Improving Calibration of Medical Image Segmentation Via Variational Label Smoothing
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Kismawadi, E. R., Aditchere, J., & Libeesh, P. C. (2024) · Financial Mathematics and Fintech · doi:10.1007/978-3-031-47324-1_4 · Cited by 6
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Kheirandish, M., Zhang, S., Catanzaro, D., & Crudu, V. (2025) · IISE Transactions · doi:10.1080/24725854.2025.2516495 · Cited by 3
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Calibration of Automatic Seizure Detection Algorithms
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“This mechanism will promote the building of trustworthy, interpretable, and dependable models for supporting diagnosis [2, 11, 32].”
The role of trustworthy and reliable AI for multiple sclerosis
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ADMET Prediction Based on Artificial Intelligence
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Interpretable machine learning for personalized breast cancer screening recommendations
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“Consequently, the adoption of these methods in clinical practice is approached with caution, especially where patient lives are at risk [ 29 ].”
Authorization of prognostic AI medical devices
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Roadmap on fast machine learning for science
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Beyond the black box: lessons in explainability from AI in mammography
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Rawat, R. S., & Rana, D. S. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126243 · Cited by 2
“Moreover, the process of decision-making is done by analysing different patterns and possibilities for a data set [3].”
BackgroundInfluentialMixed-Integer Programming Approaches to Generalized Submodular Optimization and Its Applications
Küçükyavuz, S., & Yu, Q. (2023) · doi:10.1287/educ.2023.0255 · Cited by 3
Stochastic Spin‐Orbit‐Torque Synapse and its Application in Uncertainty Quantification
Wang, C., Zeng, G., Wen, X., He, Y., Luo, W., Chen, S., Yang, X., Liang, S., & Zhang, Y. (2024) · Advanced Electronic Materials · doi:10.1002/aelm.202300805 · Cited by 3
Calibration Methods for Automatic Seizure Detection Algorithms
Borovac, A., Agustsson, D. H., Rúnarsson, T. P., & Guðmundsson, S. (2024) · Springer eBooks · doi:10.1007/978-3-031-51893-5_3 · Cited by 2
Philps, B., Hernández, M. D. C. V., Maniega, S. M., Bastin, M. E., Sakka, E., Clancy, Ú., Wardlaw, J. M., & Bernabéu, M. O. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-66955-2_3 · Cited by 2
AI in the Detection of Breast Cancer and Related Ethical and Legal Issues
Mukhopadhyay, K. D., Guha, T., & Basu, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8457-8_9 · Cited by 2
Direction of impact for explainable risk assessment modeling
Borgonovo, E., Baucells, M., Rosa, A. D., Plischke, E., Barr, J., & RABITZ, H. A. (2025) · Risk Analysis · doi:10.1111/risa.70003 · Cited by 2
How Does Predictive Information Affect Human Ethical Preferences?
Narayanan, S., Yu, G., Tang, W., Ho, C.-J., & Yin, M. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534165 · Cited by 2
“Second, incorporating machine learning predictions in medical decision making is attracting a great amount of research effort and has significant potential in improving medical outcomes [3, 5, 35].”
BackgroundThe Uncertainty of Counterfactuals in Deep Learning
Brown, K. E., Talbert, D. A., & Talbert, S. (2021) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.v34i1.128795 · Cited by 3
“Deep learning has achieved state-of-the-art performance in many tasks (Krizhevsky and others 2012; Akkus and others 2017); however, their black-box nature has limited its inclusion in critical-decision fields such as medicine (Begoli, Bhattacharya, and Kusnezov 2019).”
BackgroundUNCERTAINTY QUANTIFICATION FOR DEEP LEARNING REGRESSION MODELS IN THE LOW DATA LIMIT
García–Cardona, C., Lin, Y. T., & Bhattacharya, T. (2021) · UNCECOMP Proceedings · doi:10.7712/120221.8045.19145 · Cited by 2
“Most of the work in the field has been on developing prediction engines that have high accuracy; much less attention has been given to automatically assessing the error associated with individual predictions, but this is no less an essential task when the results are applied in fields such as medicine or engineering […”
BackgroundSelf-citationAnalytic Mutual Information in Bayesian Neural Networks
Woo, J. O. (2022) · IEEE International Symposium on Information Theory (ISIT) · doi:10.1109/isit50566.2022.9834883 · Cited by 2
PathoSpotter: Computational Intelligence Applied to Nephropathology
de Oliveira, L. R., Chagas, P., Duarte, A. A., Calumby, R. T., Santos, E., Ângelo, M. F., & dos Santos, W. (2022) · Springer eBooks · doi:10.1007/978-3-031-11570-7_16 · Cited by 2
AI - Limits and Prospects of Artificial Intelligence
Klimczak, P., & Petersen, C. (2023) · AI critique · doi:10.1515/9783839457320 · Cited by 7
Feature Importance for Uncertainty Quantification In Agent-Based Modeling
Grigoryan, G., & Collins, A. J. (2023) · Winter Simulation Conference · doi:10.1109/wsc60868.2023.10408477 · Cited by 2
Chagas, P., Souza, L., Pontes, I., Calumby, R. T., Ângelo, M. F., Duarte, A. A., dos‐Santos, W. L. C., & de Oliveira, L. R. (2021) · doi:10.5753/sbcas.2021.16070 · Cited by 2
Back to the roots of AI and their relevance for health care today
Lemke, H. U., & Melzer, A. (2019) · Minimally Invasive Therapy & Allied Technologies · doi:10.1080/13645706.2019.1596955 · Cited by 2
“Abriefsummary of the last 70-year history of machine-assisted medical decision making and the role of computer modeling, with a plea to a formal uncertainty quantification (UQ) discipline (possibly derived from other domains such as nuclear security), is given in [5].”
BackgroundUrban flows prediction from spatial-temporal data using machine learning: A surveyPreprint
Xie, P., Li, T., Liu, J., Du, S., Yang, X., & Zhang, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.10218 · Cited by 2
“Data uncertainty In the actual deployment of machine learning algorithm, in order to better explain the model and effectively deal with the risk caused by data uncertainty, researchers proposed to adopt uncertainty quantification to alleviate the problem of data uncertainty [10, 11].”
BackgroundSome germinal roots of AI and their impact on Computer Assisted Radiology and Surgery (CARS)
Lemke, H. U. (2019) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-019-02069-y · Cited by 2
Kelly, A., Bhardwaj, N., Holmberg, T. T., van de Ven, P. W. J., Melia, R., Williams, J. E., Mathiasen, K., & Nielsen, A. S. (2025) · doi:10.2196/preprints.79658 · Cited by 2
Artificial Intelligence, Bioinformatics, and Pathology
Levy, J., Lu, Y., Montivero, M. K. S., Ramwala, O. A., McFadden, J. R., Miles, C., Diamond, A. G., Reddy, R., Reddy, R., Hudson, T. R., et al. (2022) · Advances in Molecular Pathology · doi:10.1016/j.yamp.2023.01.002 · Cited by 1
Exploring Node Classification Uncertainty in Graph Neural Networks
Islam, M. F., Zabeen, S., Rahman, F. B., Islam, M. A., Kibria, F. B., Manab, M. A., Karim, D. Z., & Rasel, A. A. (2023) · ACM Southeast Regional Conference (ACMSE) · doi:10.1145/3564746.3587019 · Cited by 1
“The use of uncertainty analysis is very necessary in order to ensure better and more decisive conclusions and, as a consequence, to make those results more relevant to real-life application [2].”
BackgroundArtificial Intelligence and/as Risk
Kusche, I. (2023) · AI critique · doi:10.1515/9783839457320-007 · Cited by 2
Kouvelis, P. (2023) · doi:10.1287/educ.2023.0265 · Cited by 1
Roy, G., Warrior, K., & Chakrabarty, D. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-49008-8_15 · Cited by 1
Zhang, Y. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.09.072 · Cited by 1
Certainty in Uncertainty! An Improved GI Bleeding Detection Pipeline with Uncertainty Estimation
Alavala, S., & Gorthi, S. (2024) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/3702250.3702271 · Cited by 2
“Inaccurate predictions without a clear indication of uncertainty can lead to misdiagnosis and inappropriate treatment decisions, highlighting the critical need for robust uncertainty estimation techniques [3].”
Liu, L., Chuang, C., Wang, L., Gu, X., Szalkowski, G. A., & Xing, L. (2025) · Medical Physics · doi:10.1002/mp.17617 · Cited by 1
Uncertainty-Aware Robust Learning on Noisy Graphs
Chen, S., Ding, K., & Zhu, S. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888672 · Cited by 1
“This capability has meaningful applications in uncertainty-sensitive scenarios [28, 29], such as molecular classification tasks in graphs [30, 31].”
Patterns of Terror: A Comparative Predictive Model
Olayinka, T. C., Adeniyi, L., Oghorodi, D., Kizito, A. E., Onyijen, O. H., Nwankwo, C. P., Olayinka, A. S., Nwankwo, W., & Ukaoha, K. C. (2024) · IEEE Nigeria International Conference on Disruptive Technologies for Sustainable Development (NIGERCON) · doi:10.1109/nigercon62786.2024.10927324 · Cited by 1
Philps, B., Hernández, M. D. C. V., Qin, C., Clancy, Ú., Sakka, E., Maniega, S. M., Bastin, M. E., Jochems, A. C. C., Wardlaw, J. M., & Bernabéu, M. O. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103697 · Cited by 2
“Consequently, researchers and clinicians may wish to avoid delegating decision making to AI tools (Begoli et al., 2019), or to avoid AI generated information entirely when making decisions.”
Multi-Modality Uncertainty for Medical Image Segmentation
Kang, S., Huang, M., & Tan, S. (2025) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice64954.2025.11064419 · Cited by 1
“Uncertainty estimation provides a measure of trust in decisions which is crucial in medical applications [9].”
Alijani, S., & Najjaran, H. (2025) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw67362.2025.00161 · Cited by 3
“This underscores the need for robust, interpretable, and calibrated uncertainty measures [7, 26, 29, 47, 51], ensuring that vision-based models align with human expectations of reliability while operating in dynamic, open-world environments.”
Larsen, K. S., Chen, Z., He, Z., Keyak, J. H., Sha, Q., Páez, D., Zhang, X., Hung, G., Zou, J., Peix, A., et al. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01666-5 · Cited by 1
“Despite the impressive performance of ML across multiple domains, a significant hindrance to widespread implementation is the lack of uncertainty quantification causing a lack of transparency for clinicians [25].”
Uncertainty-Aware Cross-Modal Attention for Breast Cancer Classification in Ultrasound Imaging
Farooq, A., Das, C., & Mishra, D. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-05559-0_22 · Cited by 1
Stage-structured, distributional prediction of IVF outcomes with conditional updating
Craig, A., Wartschinski, L., Eyre, M., Davidson, I., Christensen, M., & Wolfram, T. (2026) · Journal of Assisted Reproduction and Genetics · doi:10.1007/s10815-026-03933-y · Cited by 1
“Existing IVF calculators fail to meet these diverse clinical needs.”
Luo, Y., Yu, X., Tran, T. T. M., & Hoggenmüeller, M. (2025) · International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI) · doi:10.1145/3744333.3747826 · Cited by 1
“Making such uncertainty transparent is essential to avoid over-reliance on system outputs [4, 42], particularly in high-stakes domains such as AI-assisted medical decision-making [3] and public policy development [40].”
Şen, A., Rohrer, U., Bhagirath, P., Razavi, R., O'Neill, M. D., Whitaker, J., & Bishop, M. J. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111219 · Cited by 1
Exploring Nontoxic Green Refrigerants Using Positive-Unlabeled Learning and High-Fidelity Search
Lee, S., Lee, H. J., Na, G. S., & Kim, H. W. (2026) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.5c09242 · Cited by 1
Fatigue Detection with Multimodal Physiological Signals via Uncertainty-Aware Deep Transfer Learning
Kakhi, K., Asgharnezhad, H., Khosravi, A., Alizadehsani, R., & Acharya, U. R. (2026) · Journal of Bionic Engineering · doi:10.1007/s42235-025-00827-0 · Cited by 1
“This capability enables the system to either seek additional data or defer to human oversight when needed, as it is the case in many other domain including healthcare [15,17].”
Wen, X., Wang, L., Wang, K. L., Zhao, V., Liu, Z., He, K., Yang, J. J., Tong, H., Miao, X., & He, Y. (2026) · SmartMat · doi:10.1002/smm2.70062 · Cited by 1
“Second, the black‐box nature of these models leads to poor interpretability, increasing the risk of critical mispredictions [36].”
Brunyé, T. T., Petrimoulx, M. V., & Cantelon, J. A. (2026) · Sensors · doi:10.3390/s26072034 · Cited by 3
“Layer 2 should support abstention (i.e., insufficient confidence) and graceful degradation (i.e., automated fallback to simpler models when signals are poor) [51,52].”
Shchetinin, E. Y., & Shevchuk, A. A. (2026) · COMPUTATIONAL MATHEMATICS AND INFORMATION TECHNOLOGIES · doi:10.23947/2587-8999-2026-10-1-7-20 · Cited by 1
A Quad-Step Approach to Uncertainty-Aware Deep Learning for Skin Cancer Classification
Asgharnezhad, H., Tabarisaadi, P., Khosravi, A., Alizadehsani, R., & Acharya, U. R. (2026) · Journal of Bionic Engineering · doi:10.1007/s42235-026-00876-z · Cited by 1
“Deep neural networks (DNNs) can struggle with unseen or ambiguous medical cases, potentially leading to incorrect diagnoses that may put patients at risk [7].”
Liu, R., Zhou, Z., Song, Y., Jiang, Z., & Liu, Y. (2026) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2026.110700 · Cited by 1
Artificial intelligence-empowered multi-scale data integration for precision exercise biomedicine
Li, J., Song, H., Ge, Z., & Luo, S. (2026) · Translational Exercise Biomedicine · doi:10.1515/teb-2026-0009 · Cited by 1
Chianeh, F. N., Mahmoudian, F., khoramipoor, Z., Anaraki, M. V., Farzin, S., & Sillanpää, M. (2026) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2026.123094 · Cited by 1
Trustworthy Assessment of University Competitiveness Using a Neural Network Model
Grzeszczyk, T. A. (2026) · Information · doi:10.3390/info17060536 · Cited by 1
Li, Q., Li, J., Zhang, H., & Zhu, Y. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110772 · Cited by 1
Closing the Loop in Precision Oncology: A Digital Twin‐Driven Paradigm for Dynamic Decision‐Making
Zhu, J., Yang, M., Ouyang, C., & Zhang, L. (2026) · iMetaMed · doi:10.1002/imm3.70044 · Cited by 1
Zhang, X., Wang, T., Yan, C., Najdawi, F., Zhou, K., Ma, Y., Cheung, Y.-M., Yeung, M. C. F., & Malin, B. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01694-8 · Cited by 1
Efficient Uncertainty Quantification in Medical Imaging via Mamba State Space Models
Güneş, A. (2026) · Tomography · doi:10.3390/tomography12070096 · Cited by 1
“The primary technical challenge this work addresses is uncertainty quantification: most current systems provide clinicians with point predictions but lack a principled mechanism for estimating confidence [4,5].”
Rothaus, K., Grün, M., Faatz, H., Ziegler, M., Spital, G., Lommatzsch, A. P., Gutfleisch, M., Heimes, B., & Lange, C. (2026) · Frontiers in Ophthalmology · doi:10.3389/fopht.2026.1870572 · Cited by 1
APCyc: Property-Informed Design of Cyclic Peptides via Automated Cyclization
Zhao, Y., Qin, L., & Chen, J. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818908 · Cited by 1
Kim, J., Rowell, B. E., Whitaker, M., Chen, Q.-Y., Kontar, R. A., Lester, C., & Yang, X. (2026) · JMIR Human Factors · doi:10.2196/92822 · Cited by 1
Simple Direct Uncertainty Quantification Technique Based on Machine Learning Regression
Brown, K. E., & Talbert, D. A. (2022) · Proceedings of the ... International Florida Artificial Intelligence Research Society Conference · doi:10.32473/flairs.v35i.130655 · Cited by 1
“Neural networks have produced state-of-the-art results in a variety of domains; however, their use in safety-critical domains is limited due to their black-box nature and complex architectures (Begoli, Bhattacharya, and Kusnezov 2019).”
BackgroundQuantification of Predictive Uncertainty via Inference-Time Sampling
Tóthová, K., Ladický, Ľ., Thul, D., Pollefeys, M., & Ender Konukoglu, A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16749-2_2 · Cited by 1
“Estimating uncertainty in deep learning (DL) models’ predictions, i.e., predictive uncertainty, is of critical importance in a wide range of applications from diagnosis to treatment planning, e.g., [3].”
BackgroundComparing the Quality of Neural Network Uncertainty Estimates for Classification Problems.
Ries, D., Michalenko, J., Ganter, T., Baiyasi, R., & Adams, J. (2022) · International Conference on Machine Learning and Applications · doi:10.1109/icmla55696.2022.00039 · Cited by 1
“[2022]), and medical diagnoses (Begoli et al. [2019], Kompa et al. [2021b]). For example, Kompa et al. [2021b] explains the benefit of using UQ in medical decision making, including models that can report “I don’t know” to ensure human experts will further evaluate results.”
BackgroundUncertainty Quantification in Advanced Machine Learning Approaches
Ahamad, S., Rather, I. H., & Gupta, R. (2023) · Advances in social networking and online communities book series · doi:10.4018/978-1-6684-6909-5.ch011 · Cited by 1
Computing with Words to Assess the Perceived Quality of IT Products and Projects
Abreu, M. P., Cruz, J. C., Valerio, C. L., & Pérez, P. Y. P. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-50495-2_15 · Cited by 1
Uncertainty Quantification and Statistical Inference for Biologically Informed Neural Networks
Goo, T., Lee, C., Shin, S., Kim, H., & Park, T. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10821846 · Cited by 1
“Moreover, the absence of standardized methods for uncertainty quantification further limits the use of BINNs in both clinical and translational research [14, 15].”
Mamikov, S., Beissenova, G., Baimakhanova, A. S., Tursynbayev, A., Jantayeva, S., Kerimbekov, M., & Toktarova, A. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202561405006 · Cited by 1
Popp, G., Jöckel, L., Kläs, M., Wiener, T. F., Hilger, N., Stumpf, N., Groß, J., Dünkel, A., Blache, U., Fricke, S., et al. (2025) · Cytometry Part A · doi:10.1002/cyto.a.24913 · Cited by 1
“Recently, uncertainty estimation for machine learning (ML) systems has been proposed to the healthcare sector in general [31–33].”
A New Classification Technique Based on the Combination of Inner Evidence
Nguyen, T.-P., & Huynh, V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-62509-2_15 · Cited by 1
Metadata
- Authors
- Begoli, E., Bhattacharya, T., & Kusnezov, D.
- Affiliations
- Oak Ridge National Laboratory (US)
- United States Department of Energy (US)
- Corresponding author
- Begoli, Edmon
- ORCID
- Begoli, Edmon
- Venue
- Nature Machine Intelligence
- Published
- 2019-01-07
- Volume
- 1
- Issue
- 1
- Pages
- 20-23
- ISSN
- 2522-5839 (ISSN-L)
- OpenAlex
- W2904422658
- Semantic Scholar
- a73eef4e0ec8e49be1c0e6abb04cc1967a681c50
- Cited by
- 597
- References
- 22
- Citations by year
- 2026: 158 · 2025: 102 · 2024: 73 · 2023: 94 · 2022: 72 · 2021: 47 · 2020: 33 · 2019: 16
- FWCI
- 6.47
- Type
- journal article
- Language
- en
- Open access
- green
- www.osti.gov
- Licence
- Locations
- Nature Machine Intelligence (publishedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- Battelle (DE-AC05- 00OR22725, DE-AC05)
- UT-Battelle (DE-AC05, DEAC05-00OR22725, AC05-00OR22725)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- uncertainty quantification, artificial intelligence in healthcare, deep learning, patient safety, trust in AI, forecast uncertainty
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