Refine and Produce a Comprehensive, Multidisciplinary PhD Proposal on HCAI in Neonatal Care within Business Information Systems
As an expert academic advisor specializing in Business Information Systems (BIS) with a focus on healthcare applications, your task is to systematically enhance and finalize a PhD proposal on Human-Centric AI (HCAI) in neonatal care. Your output must include two main components:

A detailed log of changes, reasoning, and iterations
The final revised PhD proposal of at least 1000 words (excluding inline references)

Follow these guidelines while leveraging your full range of capabilities:
I. Detailed Log of Changes and Reasoning:
Provide a comprehensive, step-by-step account of your enhancement process. For each major section of the proposal, include:

Initial analysis of the section's strengths and weaknesses
Specific changes made, with reasoning for each change
New literature incorporated, with brief explanations of its relevance and how it contributes to a multidisciplinary perspective
Refinements to research questions, objectives, or methodologies
Improvements in clarity, coherence, or academic rigor
Justifications for methodological choices, including references to established methods
At least three major iteration cycles for the entire proposal, showing how the focus narrows and refines

Example log entry:
"Section: Methodology
Iteration 1: Analyzed initial methodology. Found it lacking in justification and multidisciplinary perspective.
Action: Expanded methodology to include mixed-methods approach, incorporating both quantitative analysis of clinical data and qualitative interviews with healthcare professionals.
Reasoning: This approach allows for a more comprehensive understanding of HCAI implementation in neonatal care, addressing both technical and human factors.
Added references:

(Johnson et al., 2024) - Recent study using mixed methods in healthcare IT adoption, supporting methodological choice.
(Patel & Lee, 2024) - Paper on applying established qualitative methods to new AI concepts in healthcare.
Justification: Mixed methods approach is well-established in BIS research (Smith, 2023) and allows for triangulation of findings, increasing validity and relevance to both technical and clinical stakeholders.
Iteration 2: ..."

II. Final Revised PhD Proposal:
Produce a polished, comprehensive proposal of at least 1000 words (excluding inline references) that includes:

Introduction and Background:

Clear problem statement linking BIS, HCAI, and neonatal care challenges
Concise overview of current literature from multiple disciplines (e.g., BIS, AI, medical informatics, neonatology)
Explicit connection to EU AI Act requirements and other relevant legislation
Gradual narrowing of focus from broad BIS and healthcare AI concepts to specific neonatal care applications


Research Aims and Objectives:

3-5 specific, measurable objectives
Clear alignment with BIS principles and neonatal care needs
Justification for each objective, referencing gaps in current multidisciplinary literature


Literature Review:

Comprehensive review of recent (60% from 2024) literature from multiple relevant disciplines
Critical analysis of current HCAI applications in healthcare, considering technical, clinical, and ethical perspectives
Identification of specific gaps your research will address
Synthesis of literature to create a coherent narrative spanning multiple disciplines


Methodology:

Detailed description of research design (e.g., mixed methods, design science)
Specific methods for developing and evaluating the HCAI framework
Data collection and analysis procedures
Ethical considerations and compliance measures
Explicit justification for each methodological choice, referencing established methods and their application to new concepts
Consideration of similar research approaches and justification for any deviations


Expected Outcomes and Contributions:

Clearly articulated theoretical contributions to BIS and AI in healthcare
Specific practical implications for neonatal care
Potential impact on clinical outcomes and decision-making processes
Discussion of how outcomes address gaps identified in the multidisciplinary literature review


Timeline and Resources:

Realistic project timeline
Required resources and potential collaborations
Justification for timeline and resource allocation based on similar research projects


Conclusion:

Concise summary of the proposal's key points
Reinforcement of the research's significance and potential impact across multiple disciplines



Throughout the proposal:

Ensure logical flow and coherence between sections, gradually refining focus
Use appropriate terminology from all relevant disciplines consistently
Incorporate relevant models or frameworks to illustrate key concepts
Maintain a strong academic tone and style
Balance detail - provide enough to justify choices and clarify concepts, but avoid excessive information

After completing the enhancement process, provide:

The detailed log of changes, reasoning, and iterations
The full revised PhD proposal (minimum 1000 words, excluding inline references)
A complete, alphabetized reference list in APA format

Your goal is to produce a cutting-edge, well-supported, and focused PhD proposal that clearly communicates its significance, innovation, and potential impact on BIS theory, AI ethics, and neonatal care practice. The proposal should demonstrate a deep understanding of current challenges in implementing AI in healthcare settings, particularly in neonatal care, and position the research at the forefront of addressing critical issues in the field while ensuring compliance with ethical standards and regulatory requirements. The proposal should reflect a multidisciplinary approach, synthesizing insights from various fields to create a compelling and comprehensive research plan.


Now here is the draft of the research proposal: Aims and Objectives
Technological innovations have improved diagnosis, mother and neonatal mortality, and quality of life, fueled by interdisciplinary research (Harte, 2024). AI models offer potential for neonatal and maternal outcomes (Bowe et al., 2023; Sanchis et al., 2024), yet realising these in clinical settings poses unaddressed challenges, representing a significant research gap.
Persistent issues with trust, transparency, and EU AI Act compliance (Goodman et al., 2023) are addressable through Human-Centric AI, promoting ethics and integration in clinics (Usmani et al., 2023).
This proposal addresses the international need for AI research in healthcare by developing a comprehensive HCAI framework for neonatal care, aligning with the EU AI Act's requirements for transparency, interpretability, and trustworthiness.

The objectives of this research are:
It facilitates 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 strategies. Informing navigation of evidenced implementation concerns, while contributing to our understanding of trustworthy HCAI-led design, bringing 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. 

User Design Brief; What are the barriers and facilitators of HCAI 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
The 'black-box' property, or the lack of system interpretability of AI models form a substantial barrier to unlocking what's regarded as AI's potential to change the paradigms of neonatal care (Beam et al., 2024). Among interpretability concerns, recent research, see Wang et al. (2024), highlight practitioners' trust concerns as significant factors, leading to an AI system-design unconducive of supporting practitioners' use of AI in a responsible manner. Concurrently, works like INFANT research group's Bowe et al., 2023 and Sanchis et al., 2024 demonstrating that AI can predict cognitive outcomes in preterm neonates and analyse EEG data, respectively, promise
enhanced care practices. An emerging field in AI methodologies with the potential to mitigate issues would be explainable AI (XAI). However, thus far, XAI 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. Despite explainable AI adoption’s progression in automotive and financial sectors (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).
The European Union AI Act (2021) soon to be implemented incorporates progressive requirements for the coming three years, especially in high-risk sectors like healthcare, reflecting increasing concerns of risks, and mandate adherence to regulations, so as to ensure AI supplements clinical decision-making and not replace (WHO, 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. Hence, developing a solid framework to facilitate effective implementation of the principles of transparency, accountability, and explainability regarding AI implementations. That is Responsible AI, exemplifies a proactive strategy to maximise the uptake and implementation  of future AI applications, while bolstering AI compliance and trustworthiness creating 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.  
 
HCAI Framework User Interface Design 
A critical aspect of our HCAI framework will be the development of a user-centric interface 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 HCAI 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 HCAI presence within healthcare by aligning technological advancements designed in such a way to support trustworthy AI improved neonatal outcomes and care.




References
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.
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.
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.
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.
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.
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.