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Review Article | DOI: https://doi.org/10.31579/2767-7370/146
*Corresponding Author: Paraschos Maniatis, Athens University of Economics and Business Patision 76 Gr-15772 Athns-Greece.
Citation: Paraschos Maniatis, (2025), The Algorithmic Doctor: Bridging the Transparency Gap in AI-Driven Healthcare, J New Medical Innovations and Research, 6(4); DOI:10.31579/2767-7370/146
Copyright: © 2025, Paraschos Maniatis. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Received: 07 March 2025 | Accepted: 28 March 2025 | Published: 03 April 2025
Keywords: artificial Intelligence (AI); healthcare; transparency; explainable AI (XAI); interpretability; algorithmic bias; trust, patient safety; ethical AI; medical decision-making
Artificial intelligence (AI) is rapidly transforming healthcare, offering the potential to improve diagnostic accuracy, personalize treatment plans, and optimize resource allocation. However, the increasing complexity and opacity of AI algorithms, particularly in deep learning models, pose a significant challenge to trust, accountability, and ultimately, patient safety. This research investigates the transparency gap in AI-driven healthcare, exploring the perceptions of healthcare professionals and patients regarding the explainability and interpretability of AI-based diagnostic and treatment recommendations. Through surveys, we examine the factors contributing to the transparency gap, the impact on trust and adoption, and potential strategies for bridging this gap through explainable AI (XAI) techniques and improved communication. The findings highlight the urgent need for enhanced transparency in AI-driven healthcare to ensure responsible and ethical deployment of these powerful technologies.
The integration of artificial intelligence (AI) into healthcare is no longer a futuristic concept but a rapidly evolving reality. AI algorithms are being deployed across a wide spectrum of applications, from analyzing medical images to predict disease outbreaks, assisting in surgical procedures to personalizing drug therapies. The potential benefits are immense: improved diagnostic accuracy, faster treatment delivery, reduced costs, and enhanced patient outcomes.
However, the rise of AI in healthcare is not without its challenges. One of the most significant hurdles is the lack of transparency and explainability in many AI models, particularly deep learning algorithms often referred to as "black boxes." These algorithms can achieve remarkable accuracy, but their internal workings remain largely opaque, making it difficult to understand why they arrive at specific conclusions. This lack of transparency, or the "transparency gap," raises serious concerns about trust, accountability, and the potential for algorithmic bias to perpetuate existing health disparities.
This research addresses the critical need to bridge the transparency gap in AI-driven healthcare. By investigating the perspectives of both healthcare professionals and patients, we aim to understand the factors contributing to this gap, its impact on trust and acceptance, and potential strategies for fostering greater transparency through explainable AI (XAI) techniques and improved communication.
This research aims to achieve the following objectives:
1. AI in Healthcare: Benefits and Challenges
Artificial intelligence (AI) has revolutionized healthcare by enhancing diagnostic precision, personalizing treatment plans, and optimizing medical workflow efficiency (Topol, 2019). AI-powered tools such as deep learning algorithms have demonstrated remarkable success in radiology, pathology, and predictive analytics (Esteva et al., 2017). However, despite these benefits, challenges remain, including data privacy, algorithmic bias, and the lack of standardized frameworks for validation and regulatory approval (Yu et al., 2018).
2. The Transparency Gap in AI-Driven Healthcare
One of the major concerns with AI applications in medicine is the lack of transparency, particularly in deep learning models, often regarded as "black boxes" (Rudin, 2019). The complexity of these models makes it difficult to interpret their decision-making processes, creating skepticism among healthcare professionals and patients (Ghassemi et al., 2020). This opacity can lead to resistance in clinical adoption and increased liability concerns (London, 2019). Furthermore, algorithmic biases, often stemming from unrepresentative training data, exacerbate disparities in healthcare outcomes (Obermeyer et al., 2019).
3. Explainable AI (XAI) in Healthcare
Explainable AI (XAI) aims to improve model interpretability by offering insights into how AI-driven decisions are made. Several XAI techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP), and attention mechanisms, have been proposed to enhance transparency (Adadi & Berrada, 2018). Studies have shown that incorporating XAI methods can increase clinicians' trust in AI-driven diagnostics and treatment recommendations (Holzinger et al., 2017). However, the effectiveness of these techniques varies based on the complexity of the medical condition and the interpretability of the model’s outputs (Tjoa & Guan, 2020).
4. Trust in AI: Factors Influencing Adoption
Trust plays a pivotal role in AI adoption within healthcare. Research suggests that transparency, reliability, and fairness significantly impact clinicians' and patients' willingness to rely on AI-based systems (Lee & See, 2004). Additionally, a lack of standardized communication regarding AI decision-making processes can further contribute to mistrust (Caruana et al., 2015). Studies indicate that even when AI demonstrates superior performance compared to human counterparts, low interpretability can hinder its acceptance (Shortliffe & Sepúlveda, 2018).
5. Ethical Considerations and Algorithmic Bias
AI-driven healthcare systems must address ethical concerns such as data privacy, patient autonomy, and bias mitigation (Floridi et al., 2018). Algorithmic bias remains a significant challenge, as biased training datasets can lead to disparities in medical recommendations across different demographic groups (Mehrabi et al., 2021). For instance, a study by Obermeyer et al. (2019) highlighted that an AI model used for predicting healthcare needs systematically underestimated the health risks of Black patients due to biased training data. Addressing these issues requires more rigorous fairness-aware AI models and ethical oversight (Leslie, 2019).
6. Communicating AI Decisions to Non-Technical Audiences
Effective communication of AI-generated medical insights is crucial for both clinicians and patients. Studies suggest that user-friendly visualizations, simplified explanations, and standardized reporting formats can enhance comprehension and acceptance of AI recommendations (Lipton, 2018). Furthermore, integrating AI explanations within clinical decision support systems can facilitate informed decision-making and reduce clinician cognitive load (Rajkomar et al., 2019).
This research will employ quantitative collection and analysis techniques to provide a comprehensive understanding of the transparency gap in AI-driven healthcare.
Data Analysis:
This research seeks to answer the following key questions:
Results Received by The Questionnare: The questionnaire was sent to 200 individuals
Statistical Summary of AI Survey Responses
Statistical Analysis Report: Transparency and Trust in AI-driven Healthcare
1. Introduction
This report presents a statistical analysis of survey responses related to AI-driven healthcare, focusing on trust, transparency, and familiarity with AI technologies among healthcare professionals and patients. The dataset was analyzed using descriptive statistics, inferential tests (t-tests, ANOVA, correlation analysis), and regression modeling to identify key patterns and relationships.
2. Descriptive Statistics
Key Findings:
3. Inferential Statistics
3.1 T-test: Trust in AI (Doctors vs. Patients)
3.2 ANOVA: Transparency Perception Across Roles
3.3 Correlation Analysis
Conclusion: Higher AI understanding is slightly associated with higher perceived transparency, but it does not strongly predict trust.
3.4 Regression Analysis: Predictors of Trust in AI
Key Insights:
Implications:
This study highlights that while AI transparency is important, it does not directly translate to trust. Familiarity with XAI is the only factor that showed a meaningful impact on trust levels. Future AI-driven healthcare solutions should focus not just on explainability but also on ethical frameworks, clear regulations, and improved user engagement to enhance trust.
By implementing these strategies, we can bridge the transparency gap and foster trust in AI-driven healthcare solutions.
Statistical Results of Additional Statistical Tests Refining the Findings
1. Chi-Square Test: Association Between Role and Trust in AI
Interpretation:
2. Factor Analysis (PCA): Key Components of Transparency & Trust
Interpretation:
3. Multivariate Regression: Predicting Trust in AI
Interpretation:
Graphical Representations
Demographics Summary Table – Displays roles, experience, and AI interaction.
AI Understanding vs Transparency Perception (Bar Chart) – Highlights respondents' understanding of AI and their perception of transparency.
Trust in AI (Pie Chart) – Shows the distribution of trust levels in AI among respondents.
XAI Familiarity vs Importance (Comparative Bar Chart) – Compares familiarity with explainable AI (XAI) and its perceived importance.
Statistical Analysis Summary Table – Summarizes key statistical findings such as T-tests, ANOVA, correlations, and regression analysis.
Correlation Scatter Plot (AI Understanding, Transparency, and Trust) – Illustrates the relationship between AI understanding, transparency perception, and trust.
Answers On The Research Questions
Based on the statistical analysis presented in your document, here are validated answers to each research question:
1. What is the current level of awareness and understanding of AI applications in healthcare among healthcare professionals and patients?
2. What are the primary factors contributing to the transparency gap in AI-driven healthcare?
3. How does the transparency gap impact trust in AI-based diagnostic and treatment recommendations among healthcare professionals and patients?
4. To what extent can XAI techniques enhance the interpretability and explainability of AI models used in healthcare?
5. What are the most effective strategies for bridging the transparency gap through improved communication, standardized reporting practices, and ethical AI design?
Final Takeaway
The transparency gap in AI-driven healthcare is a complex issue that does not have a single solution. Trust is not solely dependent on explainability—ethical considerations, regulatory oversight, and better communication strategies are equally (if not more) important. Implementing XAI techniques helps improve interpretability, but a multifaceted approach including education, regulation, and collaboration is necessary to fully bridge the gap.
By implementing these strategies, we can bridge the transparency gap and foster trust in AI-driven healthcare solutions.
Statistical Results of Additional Statistical Tests Refining the Findings
1. Chi-Square Test: Association Between Role and Trust in AI
Interpretation:
2. Factor Analysis (PCA): Key Components of Transparency & Trust
Interpretation:
3. Multivariate Regression: Predicting Trust in AI
Interpretation:
Graphical Representations
Demographics Summary Table – Displays roles, experience, and AI interaction.
AI Understanding vs Transparency Perception (Bar Chart) – Highlights respondents' understanding of AI and their perception of transparency.
Trust in AI (Pie Chart) – Shows the distribution of trust levels in AI among respondents.
XAI Familiarity vs Importance (Comparative Bar Chart) – Compares familiarity with explainable AI (XAI) and its perceived importance.
Statistical Analysis Summary Table – Summarizes key statistical findings such as T-tests, ANOVA, correlations, and regression analysis.
Correlation Scatter Plot (AI Understanding, Transparency, and Trust) – Illustrates the relationship between AI understanding, transparency perception, and trust.
Answers On The Research Questions
Based on the statistical analysis presented in your document, here are validated answers to each research question:
1. What is the current level of awareness and understanding of AI applications in healthcare among healthcare professionals and patients?
2. What are the primary factors contributing to the transparency gap in AI-driven healthcare?
3. How does the transparency gap impact trust in AI-based diagnostic and treatment recommendations among healthcare professionals and patients?
4. To what extent can XAI techniques enhance the interpretability and explainability of AI models used in healthcare?
5. What are the most effective strategies for bridging the transparency gap through improved communication, standardized reporting practices, and ethical AI design?
Final Takeaway
The transparency gap in AI-driven healthcare is a complex issue that does not have a single solution. Trust is not solely dependent on explainability—ethical considerations, regulatory oversight, and better communication strategies are equally (if not more) important. Implementing XAI techniques helps improve interpretability, but a multifaceted approach including education, regulation, and collaboration is necessary to fully bridge the gap.
The study reveals a nuanced landscape of perceptions and attitudes toward AI in healthcare, highlighting the complexities surrounding trust, transparency, and the role of explainability. While the integration of AI holds immense promise for improving healthcare outcomes, its successful adoption hinges on addressing the concerns of healthcare professionals and patients.
7.1 Awareness and Understanding of AI
The survey data indicates a mixed level of awareness and understanding of AI applications in healthcare. While a notable proportion of respondents self-reported a high understanding, a significant number, particularly patients, have had limited direct interaction with AI-driven applications. This disparity suggests that while there is a growing awareness of AI's potential, practical exposure and understanding of its capabilities remain unevenly distributed. This lack of hands-on experience may contribute to skepticism and resistance to adopting AI-based recommendations.
7.2 The Transparency Gap: Multifaceted Challenges
The findings reinforce the existence of a significant transparency gap in AI-driven healthcare. This gap is not solely attributable to the technical complexity of AI algorithms but also stems from a lack of clear and accessible explanations, insufficient standardization in reporting, and concerns about algorithmic bias. The complexity of AI algorithms was identified as a major barrier to trust. While there is a demand for transparency, simply providing complex technical details may not be effective. The need for tailored and contextualized explanations is crucial.
7.3 Trust: Beyond Transparency
Contrary to initial expectations, the study revealed that transparency alone does not automatically translate to trust in AI-based recommendations. The correlation between transparency perception and trust was weak, suggesting that other factors play a more significant role. This finding challenges the common assumption that simply making AI more explainable will lead to increased acceptance and adoption. The most commonly cited factor for increasing trust was "Regulation and ethical oversight of AI." This suggests that confidence in AI systems is strongly tied to the perception that these systems are being developed and deployed responsibly, with safeguards in place to prevent harm and ensure fairness.
7.4 Explainable AI (XAI): A Promising but Not a Panacea
Familiarity with XAI techniques emerged as a potential factor influencing trust in AI. Respondents familiar with XAI were more likely to trust AI, suggesting that a better understanding of how AI makes decisions can increase confidence. The study also explored the preferred methods of XAI delivery. Participants favored visual and interactive explainability methods such as AI-generated visual explanations (charts, graphs), plain-language summaries, and interactive tools to explore AI decisions. These methods offer the potential to enhance comprehension and engagement with AI-driven insights.
7.5 Ethical Considerations and Bias Mitigation
The survey results underscore the importance of ethical considerations in AI-driven healthcare. The findings highlight a need for AI bias to be reduced. This can be done by using more diverse training data, regular audits for bias detection, clear guidelines on AI ethics, and a human review of AI decisions.
7.6 Communication is Key
The study stresses the importance of effective communication strategies for conveying AI-driven insights to both clinicians and patients. AI decision-making in healthcare should be transparent to healthcare professionals and patients. Clear, user-friendly explanations, tailored to the recipient's level of expertise, can enhance comprehension and acceptance of AI recommendations. The findings highlight the need for a shift from technical transparency to contextual explainability, focusing on the "why" behind AI decisions rather than just the "how."
This research provides valuable insights into the complex relationship between transparency, trust, and acceptance of AI in healthcare. The study's findings challenge the assumption that transparency alone is sufficient to foster trust. While explainability and XAI techniques play a crucial role in enhancing understanding, trust is ultimately shaped by broader factors, including regulatory oversight, ethical considerations, and effective communication strategies.
The study recommends focusing on AI education and training, enhancing AI communication strategies, having a regulatory and ethical oversight, and personalizing AI recommendations. Implementing these strategies can bridge the transparency gap and foster trust in AI-driven healthcare solutions.
The responsible and ethical deployment of AI in healthcare requires a multi-faceted approach that prioritizes transparency, explainability, fairness, and accountability. By addressing these challenges, we can harness the transformative potential of AI to improve healthcare outcomes and enhance patient well-being.
This study has several limitations that should be considered when interpreting the findings.
This research opens several avenues for future investigation:
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To Dear Erin Aust, I would like to express my heartfelt appreciation for the opportunity to have my work published in this esteemed journal. The entire publication process was smooth and well-organized, and I am extremely satisfied with the final result. The Editorial Team demonstrated the utmost professionalism, providing prompt and insightful feedback throughout the review process. Their clear communication and constructive suggestions were invaluable in enhancing my manuscript, and their meticulous attention to detail and dedication to quality are truly commendable. Additionally, the support from the Editorial Office was exceptional. From the initial submission to the final publication, I was guided through every step of the process with great care and professionalism. The team's responsiveness and assistance made the entire experience both easy and stress-free. I am also deeply impressed by the quality and reputation of the journal. It is an honor to have my research featured in such a respected publication, and I am confident that it will make a meaningful contribution to the field.
"I am grateful for the opportunity of contributing to [International Journal of Clinical Case Reports and Reviews] and for the rigorous review process that enhances the quality of research published in your esteemed journal. I sincerely appreciate the time and effort of your team who have dedicatedly helped me in improvising changes and modifying my manuscript. The insightful comments and constructive feedback provided have been invaluable in refining and strengthening my work".
I thank the ‘Journal of Clinical Research and Reports’ for accepting this article for publication. This is a rigorously peer reviewed journal which is on all major global scientific data bases. I note the review process was prompt, thorough and professionally critical. It gave us an insight into a number of important scientific/statistical issues. The review prompted us to review the relevant literature again and look at the limitations of the study. The peer reviewers were open, clear in the instructions and the editorial team was very prompt in their communication. This journal certainly publishes quality research articles. I would recommend the journal for any future publications.
Dear Jessica Magne, with gratitude for the joint work. Fast process of receiving and processing the submitted scientific materials in “Clinical Cardiology and Cardiovascular Interventions”. High level of competence of the editors with clear and correct recommendations and ideas for enriching the article.
We found the peer review process quick and positive in its input. The support from the editorial officer has been very agile, always with the intention of improving the article and taking into account our subsequent corrections.
My article, titled 'No Way Out of the Smartphone Epidemic Without Considering the Insights of Brain Research,' has been republished in the International Journal of Clinical Case Reports and Reviews. The review process was seamless and professional, with the editors being both friendly and supportive. I am deeply grateful for their efforts.
To Dear Erin Aust – Editorial Coordinator of Journal of General Medicine and Clinical Practice! I declare that I am absolutely satisfied with your work carried out with great competence in following the manuscript during the various stages from its receipt, during the revision process to the final acceptance for publication. Thank Prof. Elvira Farina
Dear Jessica, and the super professional team of the ‘Clinical Cardiology and Cardiovascular Interventions’ I am sincerely grateful to the coordinated work of the journal team for the no problem with the submission of my manuscript: “Cardiometabolic Disorders in A Pregnant Woman with Severe Preeclampsia on the Background of Morbid Obesity (Case Report).” The review process by 5 experts was fast, and the comments were professional, which made it more specific and academic, and the process of publication and presentation of the article was excellent. I recommend that my colleagues publish articles in this journal, and I am interested in further scientific cooperation. Sincerely and best wishes, Dr. Oleg Golyanovskiy.
