The DOI matches its Crossref record; 72 of the 78 references it cites were found in a registry or an index; none of the 20 sources its claims cite is checked for support yet.
Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare
Open accessPublished version of a preprint
Abstract
Artificial Intelligence (AI) describes computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. Examples of AI techniques are machine learning, neural networks, and deep learning. AI can be applied in many different areas, such as econometrics, biometry, e-commerce, and the automotive industry. In recent years, AI has found its way into healthcare as well, helping doctors make better decisions (“clinical decision support”), localizing tumors in magnetic resonance images, reading and analyzing reports written by radiologists and pathologists, and much more. However, AI has one big risk: it can be perceived as a “black box”, limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death. As a result, the term Explainable Artificial Intelligence (XAI) has been gaining momentum. XAI tries to ensure that AI algorithms (and the resulting decisions) can be understood by humans. In this narrative review, we will have a look at some central concepts in XAI, describe several challenges around XAI in healthcare, and discuss whether it can really help healthcare to advance, for example, by increasing understanding and trust. Finally, alternatives to increase trust in AI are discussed, as well as future research possibilities in the area of XAI.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This narrative review surveys the current status of Explainable Artificial Intelligence (XAI) in healthcare, covering the "black box" trust problem, legal/regulatory drivers, transparent versus post-hoc explainability, privacy and security trade-offs, and the tension between explainability and accuracy. The author argues that in healthcare accuracy may outweigh explainability, and that trust might better be built through robustness, fairness, reproducibility, and human–AI collaboration than through explanation alone.
Key findings
- XAI is growing rapidly in the literature: the first mention of "explainable artificial intelligence" in a PubMed title, abstract or keyword was in 2018, and it has since been mentioned 511 times, of which more than half (310) appeared in 2022 or the first months of 2023; Embase shows a similar trend (queries performed 2023-03-06).
- Explainability and accuracy can trade off: making a model fully transparent (a "glass box") may require simplification that reduces the accuracy of a highly predictive model (a "crystal ball"). Van der Veer et al. concluded citizens may value explainability in healthcare less than in non-healthcare domains and less than professionals assume, especially weighed against accuracy.
- XAI is double-edged for privacy and security: it can help detect errors and malicious behavior, but explanations may reveal sensitive information or enable manipulation via reverse engineering. Saifullah et al. recommend differential private federated learning plus perturbation-based XAI methods when both privacy and explainability are required.
- Explanations do not automatically produce trust: Le Merrer and Trédan show remote explainability cannot prevent a service provider from lying about the true reasons for its decisions, while Druce et al. found a statistically significant increase in user trust and acceptance using a three-fold explanation.
Methods
- Design: narrative review of XAI concepts, issues, and challenges in healthcare, with no systematic review protocol described.
- Data: no new data were created or analyzed; the paper draws on cited literature and a bibliometric query of PubMed and Embase for the term "explainable artificial intelligence" in titles, abstracts and keywords, performed on 2023-03-06.
- Analysis: thematic discussion organized into sections covering transparency, legal/regulatory compliance (GDPR, the EU AI Act, HIPAA, PIPL), transparent vs. post-hoc explainability, privacy/security, human–AI collaboration, trust, scientific XAI (sXAI), the explainability–accuracy balance, measurement of explainability, and future complexity.
Limitations
- The paper states no formal limitations section; it is explicitly a narrative review, and the text notes that explainability is difficult to measure because "the quality of an explanation is somewhat subjective."
- Evident from the text: the review is a non-systematic narrative synthesis by a single author, so selection of cited work is not governed by a documented search protocol.
- Evident from the text: the version provided is a preprint (preprints.org, posted 7 March 2023) marked "NOT PEER-REVIEWED," and the author is an employee of Philips Research (declared under Acknowledgements).
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
20 Own interpretation · 16 Not checked yet · 3 Own result
A result the paper reports from its own data — 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 results for the Embase database show a similar trend.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 1. Introduction
plaincorrelational- 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.
In the paper
“Its first mention in a PubMed title, abstract or keywords was in 2018, in a paper about machine learning in neuroscience [11].”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 1. Introduction
What the sources say
- Not checked yetcites [11] Vu, 2018, A Shared Vision for Machine Learning in Neuroscience, J. Neurosci. source
plain- 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.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“XAI is being mentioned more and more in scientific publications, as can be seen in figure 1.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 1. Introduction
plain- 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.
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
“Another advantage of XAI is that it can help organizations comply with laws and regulations that require transparency and explainability in AI systems.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 3. Legal and regulatory compliance
plainconditional- 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.
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
“This shows the growing importance of XAI in (bio)medicine and healthcare.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 1. Introduction
plaincorrelational- 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.
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In the paper
“Within the General Data Protection Regulation (GDPR) of the European Union, transparency is a fundamental principle for data processing [15].”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 3. Legal and regulatory compliance
What the sources say
- Not checked yetcites [15] Jung, 2023, Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review, Heliyon source
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: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“With the advent of deep learning (using multiple layers) and new types of algorithms such as Deep Belief Networks (DBNs) [46] and Generative Adversarial Networks (GANs) [47], made possible by the increasing computer power, artificial intelligence algorithms are gaining complexity.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 11. Increasing complexity in the future
What the sources say
- Not checked yetcites [46] Bell, A., Nov, O., and Stoyanovich, J. (2022). Think about the Stakeholders First! Towards an Algorithmic Transparency Playbook for Regulatory Compliance. arXiv. source
- Not checked yetcites [47] HHS Office for Civil Rights (2002). Standards for privacy of individually identifiable health information—Final rule. Fed. Regist., 67, 53181–53273.
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.
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 healthcare, this balance might shift more to the 'crystal ball' as accuracy might be considered more important than transparency and explainability.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 9. 'Glass box' vs. 'crystal ball': balance between explainability and accuracy/performance
hedgedconditional- 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.
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
“Explainability is much more difficult to be measured because the quality of an explanation is somewhat subjective.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 10. How to measure explainability?
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.
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 some cases, the need for explainability can come at the cost of reduced performance of the model.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 9. 'Glass box' vs. 'crystal ball': balance between explainability and accuracy/performance
plainconditional- 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. 11
- Probably not a substantial claim. 16
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Since then, it has been mentioned a total of 511 times, of which more than half (310) in papers from 2022 or the first months of 2023.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 1. Introduction
plain- 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.
In the paper
“This trust can exist on two levels: trust in the model versus trust in the prediction [14].”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 2. From 'black box' to '(translucent) glass box'
What the sources say
- Not checked yetcites [14] Tjoa, 2020, A survey on explainable artificial intelligence (xai): Toward medical xai, IEEE Trans. Neural Netw. Learn. Syst. source
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.
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
“A low opacity of the translucent glass box (or high transparency of the model) can lead to a better understanding of the model, which in turn could increase trust.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 2. From 'black box' to '(translucent) glass box'
hedgedconditional- 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.
- Rests on an assumption: AI algorithms can be perceived as black boxes, and this limits trust in their reliability, which is a major issue in healthcare where decisions can be life-or-death.
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
“Therefore, explainability might be considered of less importance in healthcare, compared to accuracy.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
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.
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Probably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The post-hoc models need to explained by resorting to diverse means to enhance their interpretability, such as text explanations, visual explanations, local explanations, explanations by example, explanations by simplification and feature relevance explanations techniques.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 4. Explainability: transparent or post-hoc
What the sources say
- Not checked yetcites [22] Vale, 2022, Explainable artificial intelligence (XAI) post-hoc explainability methods: Risks and limitations in non-discrimination law, AI Ethics source
plaindirect- 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.
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
“Within healthcare, trust is a fundamental issue because important decisions might be taken based on the output of the AI algorithm.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 7. Do explanations always raise trust?
hedgeddirect- 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.
- Rests on an assumption: AI algorithms can be perceived as black boxes, and this limits trust in their reliability, which is a major issue in healthcare where decisions can be life-or-death.
In the paper
“The EU is currently working on the Artificial Intelligence Act [17] which makes a distinction between non-high-risk and high-risk AI systems.”
- Also stated in: § 3. Legal and regulatory compliance · plain
“On non-high-risk systems only limited transparency obligations are imposed, while for high-risk systems many restrictions are imposed on quality, documentation, traceability, transparency, human oversight, accuracy and robustness.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 3. Legal and regulatory compliance
What the sources say
- Not checked yetcites [17] 2019, Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view, IEEE Access source
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.
- Also stated in: § 3. Legal and regulatory compliance · plain
In the paper
“Linear models and rule-based models are very transparent, but usually have a lower performance than deep learning algorithms (figure 5 of [39]).”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 9. 'Glass box' vs. 'crystal ball': balance between explainability and accuracy/performance
What the sources say
- Not checked yetcites [39] Solayman, 2023, Automatic COVID-19 prediction using explainable machine learning techniques, Int. J. Cogn. Comput. Eng.
plaincorrelational- 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.
In the paper
“Combining explainability of algorithms with privacy-preserving methods such as federated learning [27] might help.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 5. Privacy and security: a mixed bag
What the sources say
- Not checked yetcites [27] 2021, Dissecting scientific explanation in AI (sXAI): A case for medicine and healthcare, Artif. Intell. source
hedgedconditional- 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.
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
“A bias could be introduced by having a training dataset with low diversity, or by subjective responses of clinicians to a questionnaire.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
hedgedconditional- 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.
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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
“End users just want to be sure that the predictions done by the algorithm are accurate, which can be proven by showing them correct predictions from the past.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
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. 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
“Robustness of an algorithm in healthcare can be proven by presenting good results based on long-term use in different patient populations.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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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
“This increasing complexity might make it almost impossible for end users in healthcare (clinicians as well as patients) to understand and trust the algorithms.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
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.
- Rests on an assumption: AI algorithms can be perceived as black boxes, and this limits trust in their reliability, which is a major issue in healthcare where decisions can be life-or-death.
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
“However, making AI explainable is a difficult task, and it will be even more difficult when the complexity of AI algorithms will continue to increase.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
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
“With algorithms getting more complex, it might also be more difficult to make them explainable.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 11. Increasing complexity in the future
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.
In the paper
“For example, trust in algorithms could also be raised by ensuring robustness and by encouraging fairness [50].”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
What the sources say
- Not checked yetcites [50] Charmet, 2022, Explainable artificial intelligence for cybersecurity: A literature survey, Ann. Telecommun. source
hedgedconditional- 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: 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’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Van der Veer et al. [40] concluded that citizens may indeed value explainability of AI systems in healthcare less than in non-healthcare domains and less than often assumed by professionals, especially when weighed against system accuracy”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 9. 'Glass box' vs. 'crystal ball': balance between explainability and accuracy/performance
What the sources say
- Not checked yetcites [40] Gao, 2022, A review of the application of deep learning in the detection of Alzheimer's disease, Int. J. Cogn. Comput. Eng.
hedgedconditional- 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
“Fairness of an AI algorithm is concurrent with bias minimization.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
plaincorrelational- 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.
None of the sources cited for this claim have been checked yet.
In the paper
“Privacy laws such as GDPR, HIPAA and PIPL all include clauses that state that the handling of healthcare data should be transparent, which means that AI algorithms that work with these data should be transparent and explainable as well.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
What the sources say
- Not checked yetcites [15] Jung, 2023, Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review, Heliyon source
- Not checked yetcites [19] Lim, 2022, Explainable AI, but explainable to whom? An exploratory case study of xAI in healthcare, Handbook of Artificial Intelligence in Healthcare: Practicalities and Prospects
- Not checked yetcites [21] Phillips, P.J., Hahn, C.A., Fontana, P.C., Broniatowski, D.A., and Przybocki, M.A. (2020). Four Principles of Explainable Artificial Intelligence, National Institute of Standards and Technology. source
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.
In the paper
“Arrieta et al. [22] classified studies on XAI into two approaches: some works focus on creating transparent models, while most works wrap black-box models with a layer of explainability, the so-called post-hoc models.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 4. Explainability: transparent or post-hoc
What the sources say
- Not checked yetcites [22] Vale, 2022, Explainable artificial intelligence (XAI) post-hoc explainability methods: Risks and limitations in non-discrimination law, AI Ethics source
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.
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
“Especially in healthcare, accuracy (and to a lesser extent performance) is crucial as it could be a matter of life and death.”
§ Explainable Artificial Intelligence (XAI) in Healthcare > 12. Discussion
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. It may state two separate claims as one.
The paper relies on this without support.
AI algorithms can be perceived as black boxes, and this limits trust in their reliability, which is a major issue in healthcare where decisions can be life-or-death.
“However, AI has one big risk: it can be perceived as a 'black box', limiting trust in its reliability, which is a very big issue in an area in which a decision can mean life or death.”
“They can be perceived as a 'black box': something goes in, and something comes out, with no understanding of what happens inside.”
“This distrust in AI algorithms, their accuracy and reliability, is a very big issue in an area in which a decision could mean life or death of the patient.”
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?
In the citing paper
“Explainable AI can enhance trust by making AI recommendations understandable [304].”
From AI Fairness in Data Management and Analytics: A Review on Challenges, Methodologies and Applications (2023) · cites this paper as [304]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Explainable Artificial Intelligence (XAI) in Healthcare > References
“2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)”
§ Explainable Artificial Intelligence (XAI) in Healthcare > References
“2018, 293-300.”
Citations
Cites
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · cited 1×
“[20] classified studies on XAI into two approaches—some works focus on creating transparent models, while most works wrap black-box models with a layer of explainability, the so-called post-hoc models (Figure 2).”
BackgroundLipton, Z. C. (2018) · Queue · doi:10.1145/3236386.3241340 · Cited by 5,427
Artificial intelligence in healthcare
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“[35] discuss the multi-granularity three-way decisions paradigm [36] and how this acts as a part of granular computing models, playing a significant role in explainable decision-making.”
BackgroundA Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI
Tjoa, E., & Guan, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3027314 · Cited by 2,524
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
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XAI—Explainable artificial intelligence
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“As a result of this distrust, the term Explainable Artificial Intelligence (XAI) [10] has been gaining momentum as a possible solution.”
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“Combining the explainability of algorithms with privacy-preserving methods such as federated learning [52] might help.”
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Li, J., Hua, P., Zeng, S., Peng, Q., Wu, W., Lv, L. F., Yue, L., Zhong, H. J., & Weihong, H. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-095460 · Cited by 5
Exploring DeepDream and XAI Representations for Classifying Histological Images
Martinez, J., Neves, L. A., Longo, L. C., Rozendo, G. B., Roberto, G. F., Tosta, T. A. A., de Faria, P. R., Loyola, A. M., Cardoso, S. V., Silva, A. B., et al. (2024) · SN Computer Science · doi:10.1007/s42979-024-02671-1 · Cited by 6
“XAI ensures that decisions resulting from computational algorithms based on artificial intelligence are understood by specialists [50, 51].”
Grillo, G., Torda, T., Voena, C., Ciardiello, A., & Giagu, S. (2024) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms61543.2024.00060 · Cited by 6
Yu, X., Chen, Y., Zhao, Q., & Li, H. (2025) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.388626 · Cited by 4
Artificial Intelligence for Myocardial Infarction Detection via Electrocardiogram: A Scoping Review
Bdir, S., Jaber, M., Tanbouz, O., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14196792 · Cited by 4
“Compatibility with clinical practice and gaining clinician trust will be equally crucial to successful implementation [24].”
Alzate‐Ricaurte, S., Osorio, F. O., Costa-Roig, A., Gómez-Ayala, M. C., Gomez, I. E., Arnold, M., Campos, C. I., Sánchez-Sosa, S., Lovell, M. A., Cruz, G., et al. (2026) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2026.162932 · Cited by 4
Warrier, S. P., Narasimhan, V., Meijer, E., Gütlin, Y., Nolte, O., Veeraraghavan, B., & Egli, A. (2026) · Frontiers in Public Health · doi:10.3389/fpubh.2026.1776922 · Cited by 4
Ranjan, S., & Singh, S. K. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125997 · Cited by 3
Bhongade, A., Dubey, Y. K., Palsodkar, P., et al. (2024) · IEEE Pune Section International Conference (PuneCon) · doi:10.1109/punecon63413.2024.10895118 · Cited by 4
“Privacy requirements such as GDPR, HIPAA, and PIPL require transparency in healthcare data processing, as well as explainability and transparency in AI algorithms employed with such data[9].”
Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review
de Oliveira, G. S., Rodrigues, F. H. D. S., Pontes, J. G. D. M., & Tasić, L. (2025) · Bioengineering · doi:10.3390/bioengineering12101039 · Cited by 4
“Clinicians, psychologists, and counsellors are more likely to adopt AI as a diagnostic assistant when they understand how a model arrives at its conclusions, enhancing and supporting human decision-making [36,37].”
Investigating Self-Rationalizing Models for Commonsense Reasoning
Rancourt, F., Vondrlik, P., Maupomé, D., & Meurs, M. (2023) · Stats · doi:10.3390/stats6030056 · Cited by 5
“Exercising transparency is a key concern in explainable AI (XAI) research, which seeks to make AI methods and their models more interpretable by human observers in order to maintain intellectual oversight on them [5–7].”
A Voting Approach for Explainable Classification with Rule Learning
Nössig, A., Hell, T., & Moser, G. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-63223-5_12 · Cited by 7
Integrating AutoGluon for Real-Time Monitoring and Classification of Dental Equipment Performance
Yang, M., Li, F., & Qiu, W. (2024) · IEEE Access · doi:10.1109/access.2024.3523519 · Cited by 5
An Interpretable Approach with Explainable AI for the Detection of Cardiovascular Disease
Sapra, V., & Sapra, L. (2024) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics63763.2024.10860265 · Cited by 5
Phelan, S. E. (2025) · The Review of Austrian Economics · doi:10.1007/s11138-025-00676-4 · Cited by 5
New challenges and opportunities to explainable artificial intelligence (XAI) in smart healthcare
Shoughi, A., Dowlatshahi, M. B., & Amiri, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23979-3.00020-8 · Cited by 4
XAI Personalized Recommendation Algorithm Using ViT and K-Means
Cho, Y. (2024) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-024-01843-6 · Cited by 4
“Activities and research are continuing to make the structure of this ‘black box’ and the causal relationship between individual decisions more transparent and reliable, and that is XAI (eXplainable AI) [11].”
Artificial Intelligence in Diagnosis and Treatment
Öztatlıcı, M., Eroğlu, S., Öztatlıcı, H., & Göl, M. (2024) · Gaziantep Islam Science and Technology University · doi:10.46871/eams.1470170 · Cited by 5
Overview of ML and Optimization Algorithms in Disease Prediction and a Comprehensive Review
Vijaya, J., Verma, P. K., Thakur, R., et al. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-1087-9.ch003 · Cited by 3
Meng, L., Zhu, W., Xu, N., & Hower, J. C. (2025) · Fuel · doi:10.1016/j.fuel.2025.136072 · Cited by 4
AI‐Driven Adaptive Security for Sensor Networks: Next‐Generation Firewalls for Attack Detection
Meegammana, N. W., & Fernando, H. (2025) · International Journal of Distributed Sensor Networks · doi:10.1155/dsn/5973480 · Cited by 3
“XAI techniques will be adopted to enhance system transparency, trust, and diagnostic capabilities in critical infrastructure deployments [36].”
Lawan, A. A., Abdullahi, H. Z., Abdullahi, A. Y., & Tahir, S. (2025) · OBM Neurobiology · doi:10.21926/obm.neurobiol.2503298 · Cited by 3
Aldawsari, H., & Alammar, S. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105726 · Cited by 5
Reclaiming XAI as an Innovation in Healthcare: Bridging Rule-Based Systems
Daovisan, H., Suwanwong, C., Prasittichok, P., Prayai, N., & Choowan, P. (2025) · Algorithms · doi:10.3390/a18090586 · Cited by 4
“The urgency of advancing XAI—understood as understandable, usable, interpretable, responsible, and accountable artificial intelligence—has been generated by the convergence of rapid algorithmic adoption with intensifying regulatory, ethical, and trust imperatives [11].”
Nazar, M., Unar, S., Ahmed, A., Su’ud, M. M., Alam, M., & Rahmat, A. (2026) · IEEE Access · doi:10.1109/access.2026.3664500 · Cited by 3
“Additionally, studies by Hulsen et al. [16] and D. Saraswat et al. [17] explore various explanation interfaces used by clinicians in healthcare settings.”
Alzuabidi, I. A., Mahmood, H. E., Hussein, R. R. A., Ahmed, A. A., Al-Dmour, N. A.-H., Najim, A. H., & Abdeldayem, M. M. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.01.101 · Cited by 4
Security and Privacy of Large Language Models: Threat Taxonomy, Ethical Implications, and Governance
Pribisalić, M., & Martinčić-Ipšić, S. (2026) · AI · doi:10.3390/ai7050152 · Cited by 9
Bashir, M., El–Sappagh, S., Woo, S. S., Kim, D. I., & Abuhmed, T. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104507 · Cited by 5
Explainable AI (XAI) in Healthcare - Tools and Regulations
Raif, P., Suchanek-Raif, R., & Tkacz, E. (2023) · IEEE EMBS Special Topic Conference on Data Science and Engineering in Healthcare, Medicine and Biology · doi:10.1109/ieeeconf58974.2023.10405096 · Cited by 5
“Hulsen [8] highlights several central concepts about XAI: progress from a 'black box' to a transparent 'glass box' (or a 'white box'), two approaches to developing XAI models ('transparent models' and co-called 'post-hoc models'), problem of collaboration between humans and AI, and possible…”
BackgroundAsad, A., Sarwar, M., Aslam, M., Akpokodje, E., & Jilani, S. F. (2025) · Applied Sciences · doi:10.3390/app15116152 · Cited by 6
Exploring Explainable AI in Healthcare
Padma, A., Chithanuru, V., Uppamma, P., & Vishnukumar, R. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-5468-1.ch011 · Cited by 3
Singh, G., & Pal, A. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-2839-8_12 · Cited by 4
Explainable models for predicting crab weight based on genetic programming
Shi, T., Meng, L., Deng, L., & Li, J. (2025) · Ecological Informatics · doi:10.1016/j.ecoinf.2025.103131 · Cited by 4
Optimal feature selection and model explanation for reef fish sound classification
Barroso, V. R., Lessa, A. A., Ferreira, C. E. L., & Xavier, F. C. (2025) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2024.0055 · Cited by 3
Loganathan, G. B., Palanivelan, M., Kumaran, N., et al. (2024) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan62807.2024.10894177 · Cited by 2
“Additionally, the proposed model predictions are analyzed by XAI tools [15].”
Ethical AI Redefined: Pioneering Transparency with Hybrid Explainable Models
Gayathri, J., Chandran, K. S. V., Mala, F. D. R., et al. (2025) · doi:10.1109/icmsci62561.2025.10894436 · Cited by 2
“These limitation calls for further enhancement and improvement of XAI, to be both optimally functional and accessible [8].”
Setegn, G. M., Engda, A. A., Worku, W. K., et al. (2024) · doi:10.1109/nigercon62786.2024.10927388 · Cited by 2
Mirchandani, Y. (2025) · doi:10.38124/ijisrt/25aug952 · Cited by 2
Grad-CAM-Driven Explainable Deep Learning Framework for Cervical Cancer Image Classification
Tran, H.-D.-N., Pham, T.-T., Trinh, M.-H., Ngo, T.-N., Nguyen, T., & Pham, H.-A. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-4957-3_25 · Cited by 2
TagGAN: A generative model for data tagging
Nawaz, M., Nasir, B., Zia, T., Hussain, Z., & Moreira, C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111385 · Cited by 2
Hummel, L., Lorenz-Artz, K. C. A. G., Bierbooms, J. J. P. A., & Bongers, I. M. B. (2025) · JMIR Medical Informatics · doi:10.2196/74240 · Cited by 4
“This reflects the broader trade-off between complex “black box” models, which yield higher predictive power but limited explainability, and simpler “glass box” models, which are more interpretable but often less accurate [75-77].”
Yang, L., Du, L., Jiang, L., Zhang, Y., & Fan, Q. (2026) · British Journal of Hospital Medicine · doi:10.31083/bjhm50385 · Cited by 2
A modern radiologist’s guide to artificial intelligence
Kapur, J., Kelly, B. S., & Vega, R. (2026) · Pediatric Radiology · doi:10.1007/s00247-026-06542-0 · Cited by 3
An explainable deep learning model for dental caries detection and segmentation
Brahmi, W., Jdey, I., & Drira, F. (2026) · The Visual Computer · doi:10.1007/s00371-026-04540-9 · Cited by 1
AI-Driven Data Analytics in Information Sciences and Organizational Management
Yılmaz, A. A. (2024) · Advances in business information systems and analytics book series · doi:10.4018/979-8-3693-1058-8.ch002 · Cited by 3
Şeker, C., ÖRÜCÜ, E., & ÖNBIÇAK, A. E. (2024) · Journal of Business Research - Turk · doi:10.20491/isarder.2024.1793 · Cited by 4
The Role of Explainable AI in Enhancing Threat Intelligence Analysis in Cybersecurity Operations
(2024) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets60971 · Cited by 2
Kumar, D., Bakariya, B., Verma, C., & Illés, Z. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100165 · Cited by 2
Explainable AI in Healthcare: Methods, Concepts, and Challenges
Patel, S., & Amin, H. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-3442-9_21 · Cited by 3
Explainable AI methods to increase trustworthiness in healthcare
Savchuk, D., & Doroshenko, A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-24788-0.00003-0 · Cited by 3
Xie, S., Gan, C., & Ławniczak, A. T. (2024) · Mathematics · doi:10.3390/math12233768 · Cited by 2
The Determinants of AI Success and Failure in Modern Data-Driven Organizational Landscapes
Dasaraiahgari, V. (2025) · Journal of Computer Science and Technology Studies · doi:10.32996/jcsts.2025.7.4.73 · Cited by 2
Afif, M. M., Rahman, A., Huq, A. M. R., et al. (2025) · doi:10.38124/ijisrt/25jul042 · Cited by 2
Challenges in Adapting LLMs for Transparency: Complying with Art. 14 EU AI Act
Górski, Ł., & Ramakrishna, S. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230974 · Cited by 4
Unsupervised discovery of interpretable visual concepts
Rodrigues, C. M., Boutry, N., & Najman, L. A. (2024) · Information Sciences · doi:10.1016/j.ins.2024.120159 · Cited by 3
The Importance of Interpretability in AI Systems and Its Implications for Deep Learning
Adnan, M. (2024) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-1738-9.ch003 · Cited by 2
Cilínio, M., Pereira, M. A. G., Duarte, D., Mata, L. A., & Vieira, P. A. (2024) · AEU - International Journal of Electronics and Communications · doi:10.1016/j.aeue.2024.155339 · Cited by 1
Enhancing Cardiac AI Explainability Through Ontology-Based Evaluation
Tsolakis, N., Maga‐Nteve, C., Vrochidis, S., Bassiliades, N., & Μεδίτσκος, Γ. (2024) · International Conference on Information, Intelligence, Systems & Applications (IISA) · doi:10.1109/iisa62523.2024.10786663 · Cited by 1
“The dynamic field of XAI in healthcare domain born the challenge of evaluating the produced explanations in an objective way [6].”
Mahmood, T., Wang, Y., Khan, A. R., & Ayesha, N. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23979-3.00013-0 · Cited by 1
Explainable AI-Driven Improved Disease Prediction through Symptom Analysis with Custom BERT
Islam, M. M., Jubaer, M. T., Srizon, A. Y., Hossain, M. A., Faruk, M. F., Hasan, S. M. M., Rahman, A., & Esha, N. T. (2024) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit64611.2024.11022559 · Cited by 1
“Various studies [17], [18] emphasize the need for Explainable AI (XAI) in healthcare, suggesting that embedding explainability into models from the onset enhances trust in AI systems.”
Integrating IoMT and Federated Learning for Advanced Healthcare Monitoring in Healthcare 5.0
Almogadwy, B. S., & Alqarafi, A. (2025) · Current Pharmaceutical Biotechnology · doi:10.2174/0113892010380715250728143022 · Cited by 1
The Dark Side of Personalisation
Mathews, R., Malhotra, A., & Kaur, H. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2111-0.ch015 · Cited by 1
Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development
Bobolea, S., Hinoveanu, M. I., Dimitriu, A., Brașoveanu, M., Iliescu, C., Dinu‐Pîrvu, C., Ghica, M. V., Anuța, V., Popa, L., & Prisada, R. M. (2025) · Drug Development Research · doi:10.1002/ddr.70182 · Cited by 2
“…that are able to emulate human intelligence in performing cognitive tasks like sensory perception, learning, reasoning, problem solving, and decision ‐ making (Amisha et al. 2019; Al Kuwaiti et al. 2023; Krishnan et al. 2023; Chang et al. 2019; Habehh and Gohel 2021; Hulsen 2023; Capobianco 2022).”
Hahn, D., Mattmann, C., Klaiber, M., et al. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.09.272 · Cited by 1
Chekini, Z., Dehghani, T., Khayami, R., Jafarian, A. H., Farazestanian, M., Hassanzadeh, M., & Pasdar, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-25901-9 · Cited by 2
“In this study, we established an integrated pipeline that combines ML, DL, and XAI techniques to identify potential diagnostic biomarkers for EOC from DEGs.”
InfluentialInterpreting CNN Models by Using XAI Techniques
Biswas, A., Paul, R., & Mondal, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9239-2_16 · Cited by 1
Regulatory, Ethical, and Data Challenges
Shaikh, K., Thanki, R. M., & Shaikh, A. (2026) · Synthesis lectures on engineering, science, and technology · doi:10.1007/978-3-032-10277-5_6 · Cited by 1
Trustworthy and Explainable AI: Bridging Algorithms and Ethics
Burla, S., & Priya, S. S. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330323 · Cited by 1
“This includes fairness-aware algorithms and adversarial training modules and explanation generators [20-22].”
MH ‐ XAI : Hybrid Deep Learning and XGBoost Explainable AI Model for Mental Health Prediction
Priyanka, Nagpal, S., & Sabharwal, S. (2026) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.70634 · Cited by 1
Sani, J., Alhur, A., & Ahmed, M. M. (2026) · Journal of Epidemiology and Global Health · doi:10.1007/s44197-026-00525-y · Cited by 1
“SHAP also addresses the black-box limitation of many ML models by explaining which predictors drive model decisions and how they influence individual predictions (16, 39, 40).”
Inherent causal indeterminacy: on causal responsibility of AI-based care robots
Kropf, M. (2026) · AI and Ethics · doi:10.1007/s43681-026-01073-8 · Cited by 1
Lan, Z., Lempereur, M., Bey, A. A. E., Brochard, S., & Rousseau, F. (2026) · Scientific Reports · doi:10.1038/s41598-026-46243-0 · Cited by 2
KI-Algorithmen – intraoperatives hämodynamisches Monitoring von morgen?
Dehbozorgi, M., Zental, N., Larmann, J., & Grüßer, L. (2026) · AINS - Anästhesiologie · Intensivmedizin · Notfallmedizin · Schmerztherapie · doi:10.1055/a-2617-6321 · Cited by 1
“…Kompetenzen wie Letztendlich sind Transparenz über die zugrunde liegen-den Datensätze, die Funktionsweise, die Performance und die Limitationen des eingesetzten Systems sowie entspre-chende Schulungsmaßnahmen des medizinischen Perso-nals die Implementierung im klinischen Alltag essen-ziell [36].”
Empowering healthcare 5.0 with deep learning: techniques, trends, and future directions
Maji, P. K., Chakraborty, S., Sadiq, A., Basu, S., & Ghosh, K. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-026-11593-8 · Cited by 1
Pradhan, K., & Anoop, S. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01534-3 · Cited by 1
Lai, C.-Y., Yu, J., Lee, C., Wang, G., Perdana, S. A., Chen, T.-H., & Yeh, T.-S. (2026) · Service Science · doi:10.1287/serv.2025.0067 · Cited by 1
Yan, W., Qingqing, C., Yuan, L., Jiahui, C., Hao, L., Jinyi, L., Zhiming, W., Yinman, W., Zhifeng, Y., Shilong, Z., et al. (2026) · Clinical Cancer Bulletin · doi:10.1007/s44272-026-00065-3 · Cited by 1
“First, the rarity of ICI-associated myocarditis limits the availability of high-quality labeled data, and insufficient data standardization across centers constrains model generalizability [157, 158].”
Explainable AI for subsurface carbon capture, utilization, and storage systems: A review
Davoodi, S., Moosazadeh, M., Imasuly, G. B., Al-Shargabi, M., Wood, D. A., & Burnaev, E. V. (2026) · Fuel · doi:10.1016/j.fuel.2026.140588 · Cited by 1
Das, I. J., Debata, J., Bhatta, K., Panda, S., & Samal, H. B. (2026) · Wound Repair and Regeneration · doi:10.1111/wrr.70187 · Cited by 2
Beyond Pattern Recognition: Call for Functionally Aware AI for Anatomical Illustration
Kawalec, A., & Simka, M. (2026) · JMIR Medical Education · doi:10.2196/92700 · Cited by 1
From Algorithms to Ethics: XAI’s Impact on E-Commerce
Gaur, L. (2024) · Studies in computational intelligence · doi:10.1007/978-3-031-55615-9_8 · Cited by 1
Interactive and Explainable Robot Learning: A Comprehensive Review
Seraj, E., Lee, K. M., Zaidi, Z., Xiao, Q., Li, Z., Nascimento, A., van Waveren, S., Tambwekar, P., Paleja, R., Das, D., et al. (2024) · Foundations and Trends in Robotics · doi:10.1561/2300000081 · Cited by 2
Modelling Activities of Daily Living Using Local Interpretable Model-Agnostic Explanation Algorithm
Onyekwe, H., Ekerete, I., García-Constantino, M., & Nugent, C. (2024) · Irish Signals and Systems Conference · doi:10.1109/issc61953.2024.10602854 · Cited by 1
“This sheds light on the unique considerations and complexities of implementing XAI in healthcare settings [21].”
BackgroundOn Enhancing Usability of Hindi ATM Banking with Multi-Modal UI and Explainable UX
Bhatnagar, J. R., Agarwal, S., Bhatnagar, T. K., Chandra, H., & Aggarwal, A. (2024) · World Conference on Communication & Computing (WCONF) · doi:10.1109/wconf61366.2024.10692036 · Cited by 1
“In recent times, there have been several cases of AI and ML to improve both scalability and performance [5],[6] of several e - services such as e - finance [7], e - healthcare [8],[9] and e - Commerce [10].”
BackgroundEnhancing Crop Yield Prediction Through Explainable AI for Interpretable Insights
Goel, Y., Vishwakarma, A., Malhotra, J., Gurram, T., & Kadian, K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6318-4_40 · Cited by 3
Le, H.-S., Tran, Q.-T., & Thuan, N. H. (2025) · Springer eBooks · doi:10.1007/978-981-97-9835-3_9 · Cited by 1
Reepu, R., Kaur, G., & Sharma, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3476-9.ch018 · Cited by 1
Karakasidis, E. (2025) · Health and Technology · doi:10.1007/s12553-025-00997-1 · Cited by 1
“This problem can be solved by the use of explain-able AI (XAI) that explains the rationale, the criteria, and the features used that lead to the decision, bringing to light systematic bias or inappropriate reasoning [14, 50, 57, 58].”
Large Vision-Language Models-based Human-Understandable Explanations for Medical Image Analysis
Wang, P., & Wu, J. (2025) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml67980.2025.11333439 · Cited by 1
“A. Explainable AI Hulsen et al. [1] mentioned in his research that even if end-users have a deep understanding of an AI model, they may not necessarily trust the model.”
Guest Editorial: Big data and artificial intelligence in healthcare
Hulsen, T., & Manni, F. (2024) · Healthcare Technology Letters · doi:10.1049/htl2.12086 · Cited by 1
Self-citationVeeramani, M., Karthick, P., Venkateswaran, S., Sriman, B., Bhanu, S. T., & Devi, V. S. (2025) · Wiley eBooks · doi:10.1002/9781394249312.ch7 · Cited by 3
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“In contrast to governance-focused analyses (Hulsen 2023), Topol et al. (2025 ) emphasize outcome metrics, yet neither sys - tematically links causal perturbations to clinician-facing interfaces.”
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Conducting a Data Protection Impact Assessment in Health Science: A Comprehensive GuidePreprint
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Innovative Elemental Packaging Design Based on Machine Vision Cognition
Zhang, L. Z. L. (2024) · Journal of Electrical Systems · doi:10.52783/jes.622
“The fusion of elemental packaging and machine vision cognition in biomedical applications represents a pioneering approach at the nexus of advanced packaging techniques and artificial intelligence [12].”
BackgroundAlgorithm of Mental Model of Art Design Based on Virtual Reality Technology
Li, E. A. W. (2024) · Journal of Electrical Systems · doi:10.52783/jes.680
“The fusion of elemental packaging and machine vision cognition in biomedical applications represents a pioneering approach at the nexus of advanced packaging techniques and artificial intelligence [12].”
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INTELLIGENT MEDICAL SYSTEMS BASED ON ARTIFICIAL INTELLIGENCE
Коломоєць, С. (2024) · Scientific notes of Taurida National V I Vernadsky University Series Technical Sciences · doi:10.32782/2663-5941/2024.1.1/22
Metadata
- Authors
- Hulsen, T.
- Affiliations
- Philips (Netherlands) (NL)
- Corresponding author
- Hulsen, Tim
- ORCID
- Hulsen, Tim
- Venue
- AI
- Publisher
- MDPI AG
- Published
- 2023-08-10
- Volume
- 4
- Issue
- 3
- Pages
- 652-666
- ISSN
- 2673-2688 (ISSN-L)
- OpenAlex
- W4385766743
- Semantic Scholar
- 747ce5b1d0178cb08dcf48e43f738f1476876ed5
- Cited by
- 305
- References
- 78
- Citations by year
- 2026: 92 · 2025: 112 · 2024: 85 · 2023: 16
- FWCI
- 26.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- AI (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- explainable AI, artificial intelligence in healthcare, clinical decision support, black box problem, trust in AI, interpretability, medical image analysis, radiology report analysis
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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