Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide
Open accessAbstract
Although it is widely assumed that Artificial Intelligence (AI) will revolutionise healthcare in the near future, considerable progress must yet be made in order to gain the trust of healthcare professionals and patients. Improving AI transparency is a promising avenue for addressing such trust issues. However, transparency still lacks maturation and definitions. We seek to answer what challenges do experts and professionals in computing and healthcare identify concerning transparency of AI in healthcare? Here, we examine AI transparency in healthcare from five angles: interpretability, privacy, security, equity, and intellectual property. We respond to this question based on recent literature discussing the transparency of AI in healthcare and on an international online survey we sent to professionals working in computing and healthcare and potentially within AI. We collected responses from 40 professionals around the world. Overall, the survey results and current state of the art suggest key problems are a generalised lack of information available to the general public, a lack of understanding of transparency aspects covered in this work, and a lack of involvement of all stakeholders in the development of AI systems. We propose a set of recommendations, the implementation of which can enhance the transparency of AI in healthcare.
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AI algorithms' black box nature obscures their decision-making processes, raising concerns about reliability and potential errors.
“AI algorithms' 'black box' nature, which obscures their decisionmaking processes, raises concerns about reliability and potential errors.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The commercialization of AI technologies, transitioning from academic research to private entity control, introduces new privacy and security concerns regarding patient health information.
“Concurrently, the commercialization of AI technologies, transitioning from academic research to private entity control, introduces new privacy and security concerns regarding patient health information.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The deployment of AI in healthcare is marked by challenges like potential errors, biases, and opaque learning algorithms.
“The deployment of AI in healthcare, marked by challenges like potential errors, biases, and opaque learning algorithms, underscores the importance of tailored regulatory systems.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The involvement of major technology corporations like Google, Microsoft, and Apple in healthcare AI, requiring access to vast amounts of patient health data, amplifies privacy issues.
“The involvement of major technology corporations like Google, Microsoft, and Apple in healthcare AI, requiring access to vast amounts of patient health data, further amplifies these privacy issues.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
Despite efforts to anonymize data, AI's ability to identify individuals presents a significant privacy threat.
“Despite efforts to anonymize data, AI's ability to identify individuals presents a significant privacy threat.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
Such generative models would generate data not linked to actual individuals, allowing benefits of machine learning without risks of actual patient data.
“Such models would generate data that are not linked to actual individuals, allowing for the benefits of machine learning without the risks associated with using actual patient data [1,2,22,38].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
Generative models for creating synthetic patient data have been proposed to mitigate ongoing privacy concerns.
“Additionally, the development of generative models for creating synthetic patient data has been proposed to mitigate ongoing privacy concerns.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The risk of reidentifying anonymized patient data using advanced AI algorithms is a pressing concern.
“The risk of reidentifying anonymized patient data using advanced AI algorithms is a pressing concern that underscores the need for clear communication with patients during the consent process.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
Both patients and healthcare providers frequently encounter difficulties in understanding the decision-making processes of AI systems.
“Both patients and healthcare providers frequently encounter difficulties in understanding the decision-making processes of AI systems.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The complexity of AI-driven IoT challenges patient autonomy and trust.
“The complexity of AI-driven IoT challenges patient autonomy and trust, underscoring the need for a comprehensive approach to patient education and consent.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
AI's 'black-box' nature obscures its inner workings, poses a significant challenge, affecting trust and undermining informed consent.
“AI's 'black-box' nature, which obscures its inner workings, poses a significant challenge, affecting the level of trust and undermining the foundation of informed consent [2,38].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The potential for reidentification from anonymized data represents a significant privacy risk that must be addressed within informed consent.
“The potential for reidentification from anonymized data, as highlighted by research, represents a significant privacy risk that must be addressed within the informed consent process.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The dynamic nature of AI algorithms necessitates that informed consent be treated as an ongoing process.
“The dynamic nature of AI algorithms necessitates that informed consent be treated as an ongoing process, with patients allowed to regularly review and renew their consent in line with the evolution of AI applications in healthcare [38].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The transparency of AI systems is affected by issues surrounding IP rights, which can impact the level of insight patients have into the AI systems involved in their healthcare.
“Moreover, the transparency of AI systems is affected by issues surrounding intellectual property (IP) rights, which can impact the level of insight patients have into the AI systems involved in their healthcare.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The potential for biases in AI models, often resulting from unrepresentative data, highlights the critical need to train AI algorithms on diverse and comprehensive datasets.
“The potential for biases in AI models, often resulting from unrepresentative data, highlights the critical need to train AI algorithms on diverse and comprehensive datasets.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
AI systems can exhibit biases based on the data they are trained on, which could adversely affect patient care and outcomes.
“Patients should be informed about the potential for AI systems to exhibit biases based on the data they are trained on, which could adversely affect patient care and outcomes.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
The continuous and dynamic nature of AI systems necessitates an equally flexible and responsive consent process.
“The continuous and dynamic nature of AI systems, which learn and adapt over time, necessitates an equally flexible and responsive consent process.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
Concerns regarding equity and bias in AI systems are integral to the informed consent dialogue.
“Concerns regarding equity and bias in AI systems are integral to the informed consent dialogue.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [38]
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- Authors
- Bernal, J., & Mazo, C.
- Affiliations
- German Center for Neurodegenerative Diseases (DZNE), 39120 Magdeburg, Germany
- Dublin City University (IE)
- Corresponding author
- Mazo, Claudia
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-10-11
- Volume
- 12
- Issue
- 20
- Pages
- 10228
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4304606367
- Cited by
- 70
- References
- 80
- Citations by year
- 2026: 14 · 2025: 29 · 2024: 18 · 2023: 9
- FWCI
- 4.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DZNE Pub (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- AI transparency, artificial intelligence in healthcare, interpretability, privacy, security (computing), equity, intellectual property, trust in AI, stakeholder engagement, expert opinion, public understanding of AI
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