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).
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Claims the paper makes
0 of 20 cited sources checked · 0 supported · 0 flagged
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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIn 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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?
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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'
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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?
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably 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
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✗ Based: failsProbably 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.
Reference 31 gives conflicting conference and publication years
MinorThe reference identifies the proceedings as a 2020 conference but gives 2018 as the publication year.
“2020 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)”
“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
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“[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
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817
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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.”
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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.”
BackgroundSecure, privacy-preserving and federated machine learning in medical imaging
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“Combining the explainability of algorithms with privacy-preserving methods such as federated learning [52] might help.”
Method
Flagged first, then by citations
6 of the 78 references couldn’t be matched to a known paper.
Research works printed but not linked: 7
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Cited by
Sorted by citations
Items 201–295 of 295
Formulating Explainable UI/UX for Bank ATM and Analytic Performance of HCI
Bhatnagar, J. R., Iyer, R. P., & Agarwal, S. (2024) · International Conference on Emerging Techniques in Computational Intelligence (ICETCI) · doi:10.1109/icetci62771.2024.10704149
“In recent times, there have been several cases of AI and ML to improve both scalability and performance [4],[5] of several e-services such as e-finance [6], e-healthcare [7],[8] and e-commerce [9].”
BackgroundExplainable Artificial Intelligence in genetics: A case study
Isa, A., Iliyas, I. I., Umar, I. M., et al. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-27523-4.00001-9
Managing Output Risks From Imperfect LLMS
Sanmugam, M., & Boldiston, J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-9811-1.ch010
Ahamed, S. M., & Jabez, J. (2025) · Wiley eBooks · doi:10.1002/9781394249312.ch16 · Cited by 1
Spinal Metastasis—Imaging Using XAI and RAI Techniques
Bagada, A. A., & Patel, P. V. (2025) · Wiley eBooks · doi:10.1002/9781394302444.ch5 · Cited by 1
Hummel, L. M., Lorenz-Artz, K., Bierbooms, J. J. P. A., & Bongers, I. M. B. (2025) · doi:10.2196/preprints.74240
Unveiling explainable artificial intelligence (XAI) in advancing precision medicine: An overview
Nayak, A., Satpathy, I., & Jain, V. K. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-23979-3.00010-5
Poslon, L. (2025) · Nova prisutnost · doi:10.31192/np.23.2.4
Shifa, A., Abdelfattah, S. M., Badr, M. M., Baza, M., & Rasheed, A. A. (2025) · International Telecommunications Conference (ITC-Egypt) · doi:10.1109/itc-egypt66095.2025.11186675 · Cited by 1
“Scholars emphasize embedding principles such as fairness, autonomy, and nonmaleficence into AI systems from the design phase [10].”
Explainable AF Detection in Single-Lead Signals Acquired from Portable Smart-Enabled ECG Devices
Liberczuk, S., Garcia, P. A., Barrera, P., et al. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-032-06401-1_118
Explainable Brain Tumor Classification using CNN and GradCAM
Patel, V., Joshi, N., & Singh, B. K. (2025) · International Conference on Artificial Intelligence and Machine Vision (AIMV) · doi:10.1109/aimv66517.2025.11203573
“…and explainability techniques of AI such as LIME(Local Interpretable Model-agnostic Explanations) and SHAP(SHapley Additive exPlanations), Grad-CAM is particularly tailored for CNNs and yields more accurate spatial explanations by emphasizing the model's internal representations[20].”
The Role of Data Analytics in Optimizing Hospital Resource Allocation and Decision-making
Pattirajawane, D. (2025) · Journal Of Tropical Pharmacy And Chemistry · doi:10.30872/jtpc.vi.291
Poslon, L., & Čartolovni, A. (2025) · Springer eBooks · doi:10.1007/978-3-031-91379-2_3
Explainable Machine Learning for Predicting Shear Stress Behavior in Magnetorheological Fluids
Fadhlurrahman, F., Fadhila, R. N., Saharuddin, K. D., et al. (2025) · International Conference on Instrumentation, Control, and Automation (ICA) · doi:10.1109/ica65945.2025.11252331
“XAI has seen growing application in domains like finance [23], healthcare [24], and autonomous systems [25].”
XAI-MRI: A Multilayered Framework for Explainable AI driven Medical Risk Identification
Akre, V. L., AlHajri, L., & Raman, R. (2025) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista68122.2025.11249512
“Some hospitals already use AI to make care safer[16].”
Explainable Artificial Intelligence for Trustworthy Decision-Making in Smart Wearable Textiles
Kumar, P., Singh, M., Yadav, J., Chaudhary, S., & Saxena, P. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-4287-0.ch009
Explainable machine learning for predicting voting intentions: a study of Italian politics
Pennella, L., & Barbagli, A. G. F. (2025) · International Journal of Data Science and Analytics · doi:10.1007/s41060-025-00917-6
Kessler, T. T., Hancock, P. A., Sanders, T. L., Hancock, G. M., & Kaplan, A. D. (2025) · Springer eBooks · doi:10.1007/978-981-97-8440-0_80-1
Bharath, B., Shashidhar, V., Neerugatti, V., Kumar, B. N., Santhosh Kumar, G., & Kumaar, U. P. (2025) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca66325.2025.11280321
Alammar, S., Aldawsari, H., Al-Sahly, A. M., AlGhamdi, K. A., & Khan, M. A. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3647850 · Cited by 6
“To ensure information remains private and reduce the danger of data leakage, it is necessary to securely aggregate updates that were computed locally on each device’s dataset [12].”
Integrating Digital Twin and Explainable AI for Cyber Resilience in Critical Infrastructures
Çolak, İ., Irmak, E., Erkek, İ., et al. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13898-9_37
Neuro-Symbolic AI in Drug Discovery
Perumal, D., Vaithiyalingam, M., P., Y. S., Dharanitharan, K., Dinesh, B., Kumar, M. S., Rahmathullah, A., & K., R. J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6400-1.ch011 · Cited by 1
Shan, N., Dong, Z., & Sun, Y. (2025) · International Conference on Software, Systems and Information Technology (SSITCON) · doi:10.1109/ssitcon66133.2025.11341976
“Hulsen [9] explored some core concepts of Explainable AI (XAI), described several challenges faced by XAI in healthcare, and discussed whether it can truly advance healthcare, for instance, by enhancing understanding and trust.”
Clark, J. N., Wragg, M., Nielsen, E. E., Perelló-Nieto, M., Keshtmand, N., Ambler, M. T., Sharma, S., Bourdeaux, C., Brigden, A., & Santos‐Rodríguez, R. (2026) · doi:10.2196/preprints.91597
A Cross-Domain Benchmark of Intrinsic and Post Hoc Explainability for 3D Deep Learning Models
Chakraborty, A., Karagoz, G. N., & Meratnia, N. (2026) · Journal of Imaging · doi:10.3390/jimaging12020063 · Cited by 2
Enhancing Potato Leaf Disease Classification Using a Modified CNN with XAI
Shrestha, T. E., Rahman, S. B., Bika, S. A., et al. (2025) · doi:10.1109/compas67506.2025.11381646
“Explainable AI (XAI) provides a means to understand the reasoning and decisions made by deep learning models [10].”
Hariharan, S., Praveenchandar, J., & Sophia, D. L. (2025) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca66444.2025.11383205
“Explainable AI (XAI) encompasses techniques designed to make model decisions understandable [24].”
XAI -Driven Explainability for Cardiovascular Diseases Prediction
Andritsch, J., & Dike, J. (2026) · Journal of Informatics and Web Engineering · doi:10.33093/jiwe.2026.5.1.10 · Cited by 3
A Multidimensional Approach to Address Healthcare Ecosystem's Challenges
Chatterjee, S., Dubey, S., & Pandey, A. (2025) · World Conference on Cutting-Edge Science and Technology (WCCEST) · doi:10.1109/wccest66994.2025.11389938
“As mentioned in the works of [2, 4], a crucial component of the methodology involves addressing the explainability of AI models and incorporating XAI techniques to guarantee transparency and promote assurance.”
Cross-domain Sentimental Analysis in Healthcare using Explainable AI
Tayal, S., & Reddy, P. C. S. (2025) · Global AI Summit - International Conference on Artificial Intelligence and Emerging Technology (AI Summit) · doi:10.1109/aisummit66170.2025.11411626
Breaking the Heat Code with Transparent AI Models for Predicting Extreme Temperatures
Muraina, I. O., Aiyegbusi, E. A., Agoi, M. A., Adesanya, O. M., & Ayinde, B. O. (2025) · WSEAS Transactions on Computers archive · doi:10.37394/23205.2025.24.25
Madhavi, K. R., Rongali, S. K., Polineni, T. N. S., et al. (2026) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears67481.2026.11416589
Construction of improved LIME predictive model for the multiple healthcare data sources
Sonawane, V. R., Mishra, V. P., Musmade, B. B., Dwaramwar, P. A., & Dabhade, R. G. (2026) · Systems and Soft Computing · doi:10.1016/j.sasc.2026.200479
Interpretable Deep Learning Models for Enhancing the Prediction of Diabetes of Type 2
Tiwari, D., Kaur, G., & Singh, A. (2025) · doi:10.1109/acet67282.2025.11430320
“Similarly, in 2023, Tim Hulsen et al. analyzed the significance of Explainable AI (XAI) in healthcare by employing SHAP and LIME [15].”
Explainable AI in Healthcare: Bridging the Gap Between Doctors and Models
Khadse, C., & Puri, C. (2026) · International Conference on Image Processing and Capsule Networks (ICIPCN) · doi:10.1109/icipcn67432.2026.11438566
“We categorize XAI techniques, present case studies, discuss challenges, and outline future directions, supported by 25 references and illustrative figures [12].”
Kosuri, S., Sharma, M., Dr. Guru Basava Aradhya, S., et al. (2025) · doi:10.1109/icaiqsa67794.2025.11440403
“This challenge brings out the need to have an explainability for legal AI systems to enable reliability and acceptance [3].”
Toofanee, M. S. A. (2023) · theses.fr (ABES) · doi:10.70675/600337cazbfe4z405fz9538za725b88272b7
Introduction to explainable artificial intelligence in healthcare
Singh, A., Bhardwaj, P., Garg, P., & Singh, N. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44111-0.00003-2
Explainable artificial intelligence in a diagnostic support system
Kapoor, S., Singh, A., Garg, P., & Ramasamy, L. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44111-0.00009-3
The Evolution and Impact of Explainable Artificial Intelligence in Decision-Making
Akhil, K., Baby, V., Nagula, K. S., Mulpuri, R., Mohammed, T., & Giridhar, R. V. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-5835-3_92
Mehta, V., Maram, B., Garg, J., Kumar, N., Raut, P. T., Selvarathinam, A. L. X. R. A., & Jindal, P. (2026) · Scientific Reports · doi:10.1038/s41598-026-47769-z · Cited by 2
Sridharan, K., & Sivaramakrishnan, G. (2026) · ADMET & DMPK · doi:10.5599/admet.3321
“However, given the modest predictive performance and limited specificity demonstrated in this study, any discussion of clinical implementation must be preceded by substantial improvements in model accuracy [26].”
Explainable Multiclass Diabetes Prediction Using SMOTE and Model-Agnostic XAI
Aparna, R., Krithiga, S., & Priya, R. D. (2026) · doi:10.1109/icdsaai69492.2026.11505214
“Explainable AI has been proposed to ensure that the AI-driven decisions are interpretable and trustworthy [10-11].”
Federated and Incremental Multilingual Gynaecological Disease Diagnosis System with Explainable AI
Subbareddy, D. B. R., Basha, S. M., Swamy, N. V., Krishna, B. H., & Jaha, A. N. (2026) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai69492.2026.11505110
“Explainable AI (XAI) methods can offer clear, actionable explanations of model behaviours to both clinicians and patients, thereby raising acceptability and facilitating regulatory compliance, so it is essential to incorporate XAI into privacy-conscious pipelines in the real world [4, 5].”
PDXAI: Parkinson’s Disease Prediction by employing Deep learning with Enhanced XAI
Kaur, G., Tiwari, D., Wani, N. A., & Bedi, J. (2025) · IEEE International Conference on Recent Trends in Computing and Smart Mobility (RCSM) · doi:10.1109/rcsm67767.2025.11507197
Kessler, T. T., Hancock, P. A., Sanders, T. L., Hancock, G. M., & Kaplan, A. D. (2026) · Springer eBooks · doi:10.1007/978-981-95-3658-0_80
Explainable NLP in Healthcare: Enhancing Clinical Documentation and Information Extraction
Zamanifar, A., Taheri, A., Farhadi, A., et al. (2026) · Springer eBooks · doi:10.1007/978-3-032-15088-2_7
Future Directions—Human-AI Collaboration, Adaptive Explanations, and Regulatory Readiness
Mofrad, F. B., & Yousefzamani, M. (2026) · Springer eBooks · doi:10.1007/978-3-032-15088-2_12
Explainable AI in Judicial Decision-Making: A Hybrid LegalTech Approach
Sharma, M., Thainua, R., Kumar, P., Bansal, R., & Khan, P. (2026) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom70271.2026.11525622
“When these techniques are used within the context of law, it is possible to demonstrate what influences the recommendations made by the AI system (i.e. prior convictions, supporting evidence, statutory provisions), thus enhancing transparency and enabling adequate human oversight [8], [13].”
Artificial Intelligence in Surgery: Current Applications and Future Prospects
Josiah, P. A., Nwosu-Ijiomah, C., & Eboh, N. A. (2026) · Epidemiology and Health Data Insights · doi:10.63946/ehdi/18696
Marfo, R., & Veselý, A. (2026) · Humanities and Social Sciences Communications · doi:10.1057/s41599-026-07577-2
“Explainable arti fi cial intelligence (XAI) has emerged as a key approach to addressing the limitations of opaque machine learning models, particularly in high-stakes environments such as clinical decision-making (Hulsen, 2023).”
Yapay Zekâ Destekli Dijital Çözümlerle Menopoz Yönetimi: Kadın Sağlığında Yeni Bir Dönem
Kuduz, C., Ekrem, E. C., & Erci̇yas, Ş. K. (2026) · KTO Karatay Üniversitesi Sağlık Bilimleri Dergisi · doi:10.59244/ktokusbd.1776514
Recent Advances in Intelligent Energy Management for Design and Operation of Green Hospitals
Wang, Q., Weng, L., Wang, T., & Li, J. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3701567
“First, the “black-box” nature of DL models can hinder trust and adoption in clinical environments, motivating the development of explainable AI (XAI) mechanisms to help justify energy management decisions [97], [98].”
Explainable Artificial Intelligence for Diagnosing Skin Cancer Using Dermoscopy Images
Bhoi, S. K., Jena, K. K., Panda, S. K., & Padhi, S. (2026) · SN Computer Science · doi:10.1007/s42979-026-05156-5
“The following four XAI models are used for explaining the images [5, 32–34].”
S, S. D., & Jamuna, R. (2026) · International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) · doi:10.1109/icicv68925.2026.11554621
“Machine learning and deep learning are some of the techniques that have been utilized extensively in the healthcare field and other fields [11].”
Wang, S., Yoo, B., & Li, H. (2026) · Universal Access in the Information Society · doi:10.1007/s10209-026-01350-7
“Recent reviews on XAI in healthcare argue that explainability is valued because it can make AI-supported outputs more interpretable for clinicians and other stakeholders, thereby improving understanding of how recommendations are produced [17].”
Explainable Federated Learning Framework for Diabetic Retinopathy Severity Classification
Devi, K. S., Sekar, K., Devendar Rao, B., et al. (2026) · doi:10.1109/iciscn67954.2026.11566203
“A study [10] , investigated how federated learning and XAI intersect in radiology, where the FedProx and Grad-CAM were applied in the classification of chest radiographs.”
Eze, M., Ebiesuwa, O., Oyebola, A., Okesola, K., Ojo, A., & Mgbeahuruike, E. E. (2026) · British Journal of Computer Networking and Information Technology · doi:10.52589/bjcnit-mizpefhd
Pčolová, S., Tichá, D., Peciar, P., & Thurzo, A. (2026) · Bratislavské lekárske listy/Bratislava medical journal · doi:10.1007/s44411-026-00722-y
“Therefore, responsibility for final appliance approval, safety verification, and biological plausibility should remain with the treating clinician until validated regulatory frameworks for autonomous design are established [3, 88 – 90].”
InfluentialArtificial intelligence in healthcare: concepts and applications
Das, S., Chanda, P. B., & Maity, D. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45875-0.00008-9
Altıntaş, M. (2026) · Kafkas Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi · doi:10.36543/kauiibfd.2026.001
Early Identification of Students at Risk of Dropout: A Probabilistic Deep Learning Approach
Jihaoui, M. E., Abra, O. E. K., & Mansouri, K. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574343
“…outliers were detected using domain knowledge and removed by filtering implausible values consistent with the Moroccan middle-school context and administrative constraints: Age was restricted to [12, 19], prior-year performance indicators (CGPA_Y-1, CGPA_Y-2) to [0, 20], and ClassSize to [5, 50].”
ADUX: Improving Adversarial Attack Detection in UAV Networks Using XAI
Gupta, D., & Gupta, S. (2026) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-026-11493-7
Explainable AI for Health Care Prediction Bridging Data, Decisions and Clinical Adoptation
Arathi, Shylaja, K. R., & Prabha, R. (2026) · Springer eBooks · doi:10.1007/978-981-95-3811-9_14
Future research opportunities toward using XAI in healthcare
Tiwari, S., Dwivedi, V. K., Wadawadagi, R. S., & Siddiqui, E. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45374-8.00021-6
Agarwal, S., & Gupta, R. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45374-8.00001-0
Explainable AI for malaria classification: Enhancing transparency and trust in clinical diagnostics
Savarimuthu, X., Ruban, S., Varenya, V., Shanthini, H., Mahmoud, A., Rai, S., & Subramani, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45374-8.00003-4 · Cited by 1
Nayak, A., Satpathy, I., Panwar, A., & Filho, F. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45374-8.00015-0
An integrated framework for dengue fever prediction using CNN with SHAP
Sameswari, V., Elakkiya, J., & Azarudeen, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45374-8.00008-3
Wei, C., Su, E., Kalirai, M., Tan, A., Dushniku, P., Jones, B., Kim, Y., & Kuzminykh, A. (2026) · ACM Transactions on Computer-Human Interaction · doi:10.1145/3830401
“For instance, in high-stakes environments like healthcare [55, 84] or autonomous driving [4, 5, 27], or in situations where the end-user interacting with an AI system is a domain expert [8, 119], more detailed and precise communication of certainty is necessary to ensure safety and effectiveness.”
Mahdi, N. A., Jasim, S. S., & Daeef, A. Y. (2026) · doi:10.1109/isades69945.2026.11608121
AI-Driven Transfer Learning for Medical Imaging and Sensor-Based Healthcare
Assudani, P. J., Leema, A. A., Balakrishnan, P., Kollem, S., Pachlor, R., López‐Linares, K., Oliver, I. M., Haick, H., & Bhaiyya, M. L. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10667-8 · Cited by 2
Explainable machine learning for early classification of middle school student performance
Jihaoui, M. E., Abra, O. E. K., & Mansouri, K. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1877184
Explainable AI for Transparent Formulary Decision-Making Processes
Nidiganti, S. K. (2026) · International Conference on Computer Networks, Big Data and IoT (ICCBI) · doi:10.1109/iccbi68589.2026.11619788
“Health personnel are unable to accept and trust the predictions made in good faith by AI systems [4].”
Madhavi, I., M, A. X. V., Salaja, S., Kiubakaran, S. S., Ebenezer, V., & Shyni, S. S. (2026) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc69250.2026.11624960
Should Care Robots Answer? Rethinking Moral Answerability Beyond Humans
Kropf, M. (2026) · Nursing Inquiry · doi:10.1111/nin.70150
Ahmed, M. M., Adebayo, U. O., Okesanya, O. J., & Lucero-Prisno, Z. K. O. A. D. E., III (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1016952
Wearable neural biosensors for early detection of neurodegenerative diseases
Ahmad, I., Ravindra, K., Bhargavi, Y. B., Nagaraju, K. L., Mandava, J., Mim, T. J., & Dongare, S. A. (2026) · Discover Neuroscience · doi:10.1186/s13064-026-00302-4
“Hulsen, [49], Sadeghi, [80].”
Syakurah, R. A., Izzatika, M., Mufarikhah, M., & Fadilah, M. (2025) · Journal of Education and Community Health · doi:10.34172/jech.3495
Almufarreh, A., Ahmad, A., Arshad, M., Onn, C. W., & Kiplangat, Y. M. (2026) · Frontiers in Computer Science · doi:10.3389/fcomp.2026.1863891
Brdnik, S., Colakovic, I., & Karakatič, S. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115894 · Cited by 1
Explainable AI for multi-omics in precision medicine: a systematic review
Hasnain, M., Hameed, S., Azim, K., & Waseem, M. (2026) · Exploration of Digital Health Technologies · doi:10.37349/edht.2026.1011102
Al-Qahtani, Y., Al-Salem, F., & Al-Harbi, A. (2023) · Journal of Health Informatics and Digital Systems · doi:10.68159/z335746456
Explainable NLP Models for Clinical Decision Support: A Transformer-Based Framework
Lal, M., Bhende, M. S., Patil, S., Patil, D., Sharma, S., & Shilwant, S. V. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-28206-4_5
Yao, X., Chao, Y., Gu, X., Peng, J., Yuan, J., Shi, W., Zhang, F., & Bao, Q. (2026) · Orthopaedic Surgery · doi:10.1111/os.70429
“The legal and ethical responsibility for final therapeutic decisions rests with the clinician, necessitating a “human- in- the-loop” workflow to mitigate algorithmic errors and ensure patient safety [42, 45].”
InfluentialExplainable Artificial Intelligence (XAI) for Transparent Medical Diagnosis
Biswas, D., Singh, A. P., Singh, S., et al. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30960-0_29
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Alvarez, D., & Ticona, W. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-38797-4_11
A Review of Deep Learning Models and Explainable Artificial Intelligence for Pathology Recognition
Toala-Paz, A., Becerra-Andrade, A., Cevallos-Levicek, D., Peláez, E., Domínguez, F., & Loayza, F. R. (2026) · International Conference on eDemocracy & eGovernment · doi:10.1109/icedeg70169.2026.11695396
“Third, multimodal explainability, such as combining imaging, text, and metadata, is still underexplored, despite its importance in comprehensive clinical workflows [95].”
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Naik, V., Das, S., Vinay, M. N., Manikandan, H., & Kumar, K. M. (2026) · International Journal of Intelligent Unmanned Systems · doi:10.1108/ijius-05-2026-0176 · Cited by 1
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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A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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