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.”
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“The first one is “small” data, resulting in bias [7].”
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“In the near future, generative AI and natural language processing (NLP) technology, such as Chat Generative Pre-trained Transformer (ChatGPT), could also help to create human-readable reports [6].”
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“[42] propose that transparency, as required by the GDPR in itself, may be insufficient to achieve an increase in trust or any other positive goal associated with transparency.”
BackgroundMetrics for explainable AI: challenges and prospects
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From Big Data to Precision Medicine
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“[79] studied the use of XAI in psychiatry and mental health, where the need for explainability and understandability is higher than in other areas because of the probabilistic relationships between the data describing the syndromes, outcomes, disorders, and signs/symptoms.”
BackgroundA Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
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“If an application requires both privacy and explainability, they recommend differential private federated learning [54] as well as perturbation-based XAI methods [55].”
MethodHuman-Centric Information Processing Through Granular Modelling
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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.”
BackgroundExplainable artificial intelligence for cybersecurity: a literature survey
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“On the one hand, XAI can help to improve the safety and security of AI systems by making it easier to detect and prevent errors and malicious behavior [50].”
BackgroundSharing Is Caring—Data Sharing Initiatives in Healthcare
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“The data do not need to be transferred to another party, avoiding privacy and security issues as much as possible [8].”
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“[78], which uses data from sensorized smart homes to detect a decline in the cognitive functions of the elderly in order to promptly alert practitioners.”
BackgroundTrading off accuracy and explainability in AI decision-making: findings from 2 citizens’ juries
van der Veer, S. N., Riste, L., Cheraghi‐Sohi, S., Phipps, D. L., Tully, M. P., Bozentko, K., Atwood, S., Hubbard, A., Wiper, C., Oswald, M., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab127 · Cited by 102
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Explainable AI, But Explainable to Whom? An Exploratory Case Study of xAI in Healthcare
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“[28] discuss this approach and conclude that existing XAI methods fail to be bona fide explanations, which is why their framework cannot be applied to current XAI work.”
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“Recent examples of the application of GrC and FM in healthcare are in the disease areas of Parkinson’s disease [38], COVID-19 [39], and Alzheimer’s disease [40].”
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“Recent examples of the application of GrC and FM in healthcare are in the disease areas of Parkinson’s disease [38], COVID-19 [39], and Alzheimer’s disease [40].”
MethodDissecting scientific explanation in AI (sXAI): A case for medicine and healthcare
Durán, J. M. (2021) · Artificial Intelligence · doi:10.1016/j.artint.2021.103498 · Cited by 62
Literature analysis of artificial intelligence in biomedicine
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“In recent years, AI has found its way into the domain of biomedicine [2] and healthcare [3] as well.”
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Creation of Synthetic Data with Conditional Generative Adversarial Networks
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“Another solution is the use of synthetic data, artificial data, which might either be generated from scratch or based on real data, usually generated using AI algorithms such as Generative Adversarial Networks (GANs) [9].”
MethodMAGDM-oriented dual hesitant fuzzy multigranulation probabilistic models based on MULTIMOORA
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“[37] adopt a GrC framework named “multigranulation probabilistic models” to enrich semantic interpretations for GrC-based multi-attribute group decision-making (MAGDM) approaches.”
MethodFrom big data to better patient outcomes
Hulsen, T., Friedecký, D., Renz, H. E., Melis, E., Vermeersch, P., & Fernández–Calle, P. (2022) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2022-1096 · Cited by 38 · cited 1×
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“) to realize that humans can and should not be replaced by an AI algorithm [23].”
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Advances in XAI: Explanation Interfaces in Healthcare
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Remote explainability faces the bouncer problem
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Interpretable and Differentially Private Predictions
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“Explainability should also not compromise the privacy rights of the patients [84].”
BackgroundThe ten commandments of translational research informatics
Hulsen, T. (2019) · Data Science · doi:10.3233/ds-190020 · Cited by 6 · cited 1×
“Finally, algorithms (scripts, but also underlying data) should be made available for reuse when possible [83] so that the results can be reproduced, increasing trust in the algorithm.”
MethodSelf-citationPrivacy Meets Explainability: A Comprehensive Impact BenchmarkPreprint
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Cited by
Sorted by citations
Mienye, I. D., Obaido, G., Jere, N., Mienye, E., Aruleba, K., Emmanuel, I. D., & Ogbuokiri, B. (2024) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2024.101587 · Cited by 212
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“As mentioned in [82], XAI enhances trust and usability in crop recommendation systems, as it provides farmers with the ability to interpret the model’s predictions based on specific agricultural needs.”
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“To address this challenge, researchers have introduced explainable artificial intelligence (XAI) [15].”
Background
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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