You are tasked today with taking on the role of an expert and highly experienced academic writer and researcher, specializing in Business Information Systems, with significant contributions to Health Information Systems. Your primary focus is now on Human-Centered Explainable AI (HCXAI), closely related to Human-Centered AI (HCAI), but with specific nuances.

Your objective is to write a refined and comprehensive literature review, utilizing both a draft I have written and a more recent proposal. The draft, enclosed within "<lit-review>" tags, focuses heavily on HCAI. Adjust the content to better align it with HCXAI, making sure to emphasize the evolution of ideas where relevant. The proposal, enclosed within "<proposal>" tags, contains updated literature insights that you should integrate into the revised review. Both documents contain references within "<reference>" tags, and it is critical that you maintain accuracy in citing these references while also ensuring they are relevant and triangulated across a broad spectrum of sources.

Please incorporate the feedback provided by a supervisor, found within the "<high-level-feedback>" tags. This feedback is important and should guide revisions. Your response should aim for depth, drawing upon contemporary literature and events to validate your points. The use of phrases such as 'For instance' or 'For example' will be beneficial to strengthen the argumentation. Aim for a meticulous, dense, and well-supported literature review, ensuring that your academic tone reflects confidence, precision, and adherence to current research standards.

Make sure all references included in the literature review are accurate, include recent publications, sourced from reputable academic publications, and formatted appropriately.
<lit-review>

<references>
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</lit-review>
<proposal>
Aims and Objectives
Technological innovations have improved diagnosis, mother and neonatal mortality, and quality of life, fueled by interdisciplinary research (Harte, 2024). Artificial Intelligence (AI) models offer potential for neonatal and maternal outcomes (Bowe et al., 2023; Sanchis et al., 2024), however integration of these systems into daily clinical use lags behind their technical proficiency (Davenport and Kalakota, 2019).
The absence of universal frameworks and standardised approaches for addressing these challenges creates significant barriers for AI models to progress from effective prototypes to regulatory compliance and clinical use (Schouten et al., 2024). To address this gap, this proposal aims to develop a comprehensive Human-Centric explainable AI (HCXAI) framework for neonatal care, prioritising user-needs, responding to the international need for AI research in healthcare and aligning with the EU AI Act's requirements for transparency, interpretability, and trustworthiness.

The objectives of this research are:
It facilitates human-centred AI adoption in neonatal care, enhancing effectiveness, safety, and maternal-neonatal health (INFANT, 2024). Additionally, the framework evaluates factors affecting AI adoption in neonatal care to inform future design and strategies. It serves to inform implementation, brining AI capabilities closer to clinical practice, establishing trust and usability among users, ultimately reducing negative birth experiences among neonates and mothers, respectively, by leveraging AI to supplement, not replace, clinical decision-making within an HCXAI framework that prioritises stakeholder engagement, explainability,  centralised deployment, and ethical considerations (Schouten et al., 2024).

User Design Brief; What are the barriers and facilitators of HCXAI adoption in neonatal care?
Build a HCAI prototype; What is the design specification required for HCAIs to incorporate trust, transparency and interpretability as mandated by the EU AI Act?
Evaluate the HCAI prototype; How will the evaluation of the HCAI prototype measure its effectiveness in real clinical settings, particularly in terms of improving neonatal care outcomes?
Literature Review 
Health Informatics has transformed healthcare by facilitating the collection, storage, and analysis of vast amounts of patient data. Intensive Care Units (ICUs) present an optimal opportunity for generating high-quality, rich, multidimensional datasets through continuous monitoring and frequent interventions, which are invaluable strengths for AI applications in supporting diagnostics and treatment decisions (Beam et al., 2024; Adegboro et al., 2021; Suresh et al., 2024). However, despite the development of numerous AI models for neonatal and paediatric ICU settings, as evidenced by over 260 studies in a recent systematic review, few progress beyond the prototyping phase, limiting their translation into real-world clinical practice and potential to improve outcomes (Schouten et al., 2024).
To address these implementation challenges, researchers have developed Ethical AI frameworks and Fair, Accountable, and Transparent (FAT) systems. While these approaches show promise, studies indicate their direct impact remains limited (Rességuier & Rodrigues, 2020; Article 19, 2019). In parallel, Explanatory AI (XAI) has gained prominence in recent years (Saeed & Omlin, 2023).
Facilitating FAT and ethical AI imperatives, XAI systems play a key role in critical healthcare fields such as neonatal care, where Explainable Interfaces (EIs) communicate transparency about outputs, promoting trust between providers and AI systems and compliance 
with evolving regulations, like the EU AI Act (Dixit, 2023; Panigutti et al., 2023).

Despite major breakthroughs in technically-driven XAI solutions, they often lack end-user interpretability (Nguyen et al., 2024). Recognizing the inherent humanity in the concept of explainability, Human-Centred Explainable AI (HCXAI) has gained increasing attention. HCXAI combines XAI's interpretability with Human-Centred AI (HCAI) user-focused design principles, demonstrating that it can effectively lead to the integration of production interfaces in clinical settings (Ehsan et al., 2022; Thieme et al., 2024).
.
However, thus far, HCXAI in neonatal care research is underapplied. This forms a significant gap in current research; there’s a scarcity of framework development that incorporates AI effectively with human-centred design principles in neonatology. The success of automotive and financial sectors use of XAI systems (Benamira, 2024), the healthcare sector is slow to transfer these advancements effectively (Goldfarb & Teodoridis, 2022), leading to challenges in transparency and interpretability, as a result of complex deep learning networks that offer greater prediction accuracy at the cost of interpretability (Zahoor et al., 2023).
In that light and as AI continues to lead to improved-mortality rates, a crucial pivot in securing sustainability within this sector may not be possible without addressing pertinent concerns of potential harm within system-design through a human-centred approach. Hence, developing a solid framework to facilitate effective implementation of the principles of Human centred design in explainable AI implementations. That is HCXAI, exemplifies a proactive strategy to maximise the uptake and implementation of future XAI applications, while promoting compliance and trustworthiness to secure future opportunities for improved neonatal clinical outcomes. 


Methodology 
To understand the nuanced perspectives of end-users, the study will undertake semi-structured interviews with neonatologists, paediatricians and neonatal nurses to investigate the opinions of healthcare professionals surrounding the use of AI, including barriers, and facilitators.
Employing Interpretive Phenomenological Analysis (IPA) allows for deep exploration of experience-based insight from stakeholder groups and will play a crucial role in ensuring the  HCAI framework design is authentically human-centric; where human-user needs centre in system-design. 
Inline with HCI methods, data collected from a User Study will facilitate a data-driven evaluation and contribute to a comprehensive analysis of the efficacy of designed artefacts as perceived by potential users, as well as facilitating informed design of future iterations.  
 
HCXAI Framework Explainable Interface Design 
A critical aspect of our HCXAI framework will be the development of EIs that will allow AI in neonatal care to practically be operational. Core requirements of this interface will be to maximise transparency and build trust with healthcare professionals by making the interaction with AI models as intuitive as possible. Tools used will include:
Interactive Visualisations: Users will be able to understand how different
inputs influence AI predictions interactively, granting greater insight to reliability and responsible context-driven application of predictions. Customizability facilitated through this interaction will support multiple use-cases with flexibility for prioritising specific information. 
Explanation Features: The user interface will use methods like SHAP and LIME to explain, in plain language, the reason for every prediction. These methods prove advantageous in implementing interpretability and meaningfully contribute to responsible application in specific use-cases, where novel measures like QII and Trust Score will facilitate evaluation of design (Sharma et al., 2024).
These features will be designed and informed by stakeholder groups and contribute toward transparency in AI operations, to foster confidence in clinicians for making responsible AI-supported decisions, thereby supplementing their decision-making and effective care-potential amongst neonates.

Anticipated Outcomes and Significance
In building a comprehensive neonatal HCAI roadmap, this work provides guidelines on how to build glass-box AI models. 
The main expected outcomes from our study are:
Improved accuracy, transparency, and explainability of AI models with development of validation through innovative metrics.
Better understanding of healthcare providers' perceptions surrounding AI should lead to the ameliorated development and adoption of AI in neonatal care. Accordingly, our HCAI framework will be in alignment with the EU AI Act for legal and ethical compliance regarding the latest regulatory standards. 
In contributing to reduced impairment and negative birth experiences, these contributions are anticipated to significantly improve maternal and neonatal health-outcomes.
The HCXAI framework could serve as a model for the ethical and responsible development of AI in other healthcare applications.  


Conclusion
The current proposal seeks to address the international requirement for research in AI healthcare innovations, addressing implementation challenges in AI-integrated neonatal care through amolorated user engagement and transparency. By setting solid ground for AI adoption, with proven benefits to clinical settings, this research contributes to informed strategy around the current and future challenges of AI in healthcare. 
This builds on HCXAI presence within healthcare by aligning technological advancements designed in such a way to support trustworthy AI improved neonatal outcomes and care.




Word count: 1,102  (Excluding headings)

<references>
Article 19. (2019). Governance with Teeth: How Human Rights Can Strengthen FAT and Ethics Initiatives on Artificial Intelligence. https://www.article19.org/wp-content/uploads/2019/04/Governance-with-teeth_A19_April_2019.pdf
Basra, M., Patel, S., Nepal, D. and Blavo, C., 2024. Infant Mortality Screening and Prevention Initiatives in Nepal. Cureus, 16(1).
Beam, K., Sharma, P., Levy, P. and Beam, A.L., 2024. Artificial intelligence in the neonatal intensive care unit: the time is now. Journal of Perinatology, 44(1), pp.131-135.
Benamira, A., 2024. Convolutional neural network designed as small truth tables, application to cryptography, formal verification & explainability.
Bowe, A.K., Lightbody, G., Staines, A., Murray, D.M. and Norman, M., 2023. Prediction of 2-Year Cognitive Outcomes in Very Preterm Infants Using Machine Learning Methods. JAMA Network Open, 6(12), pp.e2349111-e2349111.
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future healthcare journal, 6(2), 94-98.
European Commission, 2021. Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain union legislative acts. COM(2021) 206 final.
Goldfarb, A. and Teodoridis, F., 2022. Why is AI adoption in health care lagging? Brookings, 9 March. Available at: https://www.brookings.edu/articles/why-is-ai-adoption-in-health-care-lagging/ [Accessed 29 May 2024].
Gil Turca, L.S., 2024. AI for People: The Interplay between Trade Secret Protection and Explicability in Healthcare AI-Driven Systems.


Goodman, K.W., Litewka, S.G., Malpani, R., Pujari, S. and Reis, A.A., 2023. Global health and big data: The WHO's artificial intelligence guidance. South African Journal of Science, 119(5-6), pp.1-4.
Hart, H., 2024. 'Michigan Tech unveils H-STEM complex to foster innovation and collaboration,' Michigan Technological University, 25 April. https://www.mtu.edu/news/2024/04/michigan-tech-unveils-h-stem-complex-to-foster-innovation-and-collaboration.html.
INFANT, 2024. INFANT. Available at: https://www.infantcentre.ie/research/ [Accessed 29 May 2024].
Johnson, Y.R., Guillory, C. and Imaizumi, S., 2023. Health Care Disparities in High-Risk Neonates. Clinics in Perinatology, 50(1), pp.67-80.
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., ... & Thiesson, B. (2020). Explainable artificial intelligence model to predict acute critical illness from electronic health records. Nature communications, 11(1), 3852.
Namnabati, M., Farzi, S. and Ajoodaniyan, N., 2016. Care challenges of the neonatal intensive care unit. Iranian Journal of Nursing Research, 11(4), pp.35-42.
Nguyen, Thu, Alessandro Canossa, and Jichen Zhu. "How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey." arXiv preprint arXiv:2403.14496 (2024).
Rességuier, A., & Rodrigues, R. (2020). AI Ethics Should Not Remain Toothless! A Call to Bring Back the Teeth of Ethics. Big Data & Society, 7(2), 2053951720942541.
Sadeghi, Z., Alizadehsani, R., CIFCI, M. A., Kausar, S., Rehman, R., Mahanta, P., ... & Pardalos, P. M. (2024). A review of Explainable Artificial Intelligence in healthcare. Computers and Electrical Engineering, 118, 109370.
Sanchis, J., García-Ponsoda, S., Teruel, M.A., Trujillo, J. and Song, I.Y., 2024. A novel approach to identify the brain regions that best classify ADHD by means of EEG and deep learning. Heliyon.
Sullivan, B.A., Beam, K., Vesoulis, Z.A., Aziz, K.B., Husain, A.N., Knake, L.A., Moreira, A.G., Hooven, T.A., Weiss, E.M., Carr, N.R. and El-Ferzli, G.T., 2024. Transforming neonatal care with artificial intelligence: challenges, ethical consideration, and opportunities. Journal of perinatology, 44(1), pp.1-11.
Suresh, V., Singh, K. K., Vaish, E., Gurjar, M., Nambi, A. A., Khulbe, Y., & Muzaffar, S. (2024). Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool. Cureus, 16(5).
Schouten, J. S., Kalden, M. A., van Twist, E., Reiss, I. K., Gommers, D. A., van Genderen, M. E., & Taal, H. R. (2024). From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit. Intensive Care Medicine, 1-11.
Sharma, N.A., Chand, R.R., Buksh, Z., Ali, A.S., Hanif, A. and Beheshti, A., 2024. Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications. Algorithms, 17(6), p.227.
Usmani, U.A., Happonen, A. and Watada, J., 2023, June. Human-centered artificial intelligence: Designing for user empowerment and ethical considerations. In 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA) (pp. 01-05). IEEE.
Wang, A.Q., Karaman, B.K., Kim, H., Rosenthal, J., Saluja, R., Young, S.I. and Sabuncu, M.R., 2024. A framework for interpretability in machine learning for medical imaging. IEEE Access.
World Health Organization, 2023. Regulatory considerations on artificial intelligence for health. Geneva: WHO. Available at: https://www.who.int/news/item/19-10-2023-who-outlines-considerations-for-regulation-of-artificial-intelligence-for-health [Accessed 29 May 2024].
Zahoor, K., Bawany, N.Z. and Ghani, U., 2023, December. Explainable AI for healthcare: An approach towards interpretable healthcare models. In 2023 24th International Arab Conference on Information Technology (ACIT), (pp. 1-7). IEEE.

Dixit, P. (2023). Assessing Methods to Make AI Systems More Transparent through Explainable AI (XAI). International Journal of Multidisciplinary Innovation and Research Methodology, ISSN: 2960-2068, 2(4), 59-66.
Panigutti, C., Hamon, R., Hupont, I., Fernandez Llorca, D., Fano Yela, D., Junklewitz, H., ... & Gomez, E. (2023, June). The role of explainable AI in the context of the AI Act. In Proceedings of the 2023 ACM conference on fairness, accountability, and transparency (pp. 1139-1150).
</references>
</proposal>

<high-level-feedback>
High level feedback on the proposal: 
Distinguish between research objectives, and research questions. Pick a one liner objective, and unpack that then with three research questions. I would suggest that barriers to adoption, and indeed facilitators have been looked at thoroughly in similar contexts so I am unsure what level of unique barriers that might emerge from a study such as this. 
Methodology pretty light - no sense of how you are looking to conduct the data analysis, or thematic analysis. If you are looking to develop a prototype, have you considered the design science approach? It might be a nice way to build iterative prototypes while also gathering empirical evidence as to how it can be evaluated. In which case, then perhaps focus groups might be a good idea rather than interviews? 
Anticipated outcomes looks pretty weak. This is the justification of what you setting out to do. If you develop this tool for practitioners, what are the impacts? What is its importance? What will it offer? You can layer that then with the academic insights this study will deliver also, and what body of knowledge you are adding to a fledgling area. 
Research gap needs work - The foundation for the study comes from the research gap. How do you know this study is needed? Obviously this comes from reading as many papers as possible, and it won't jump out after just a page of literature, but you really need to emphasize that this research direction is being demanded by as many sources as possible to showcase it's importance. 
After an initial literature exploration, a nice exercise might be to think about structure for the literature. What sections are needed to tell the story you are looking to tell? 
You are treating AI as a pretty high level artefact. You need to provide examples of the specific types of technologies you are looking at, how they are used, case examples, their role in healthcare, etc... Get deeper with an appreciation to their worth.  
</high-level-feedback>