the phd research proposal format

the phd research proposal format

Exploring the Impact of Artificial Intelligence on Healthcare: A Multi-Disciplinary Approach

1. Introduction

Artificial intelligence (AI) algorithms have become ubiquitous and promise to automate, supplement, or in some cases, supplant work previously performed by radiologists and pathologists. Although AI-assisted diagnosis in histopathology, radiology, and dermatology are the major areas in which AI development is underway, they are by no means the only applications. AI is also being used to improve the drug discovery process, develop patient stratification tools for precision medicine, monitor clinical trials for safety or efficacy signals, and deliver effective public health messaging.

The Past, Present, and Future section provided a broad overview of the role of AI in guiding tasks performed by healthcare professionals. The discussion in that section was intentionally relatively detailed to provide physicians street cred when discussing AI with clinicians. In this section, we will scale back and present an overview of the anticipated impact of AI on healthcare in an accessible manner. The purpose of the different sections is to expand or narrow in at different points of the healthcare process. Throughout the global healthcare system, it can be difficult to operationalize, employ, or conserve relevant or filtered healthcare data. The value of data is often lost due to data rights, privacy controls, privacy checklists, or failure to understand the value or use of healthcare data.

2. Literature Review

As with other technologies used in the field of health and medicine, AI is primarily concerned with assisting, augmenting, and optimizing the efforts of human health professionals as they seek to advance health and well-being for every human on the planet. Popular approaches in AI are progressing rapidly, and assistive intelligence is widely expected to be an efficient tool for recognizing the best next step for diagnosis, treatment, and care in order that the health system as a whole can be further advanced. There is no question that AI, among other digital health and communications technologies, will play an increasingly important role in healthcare. The practical implications of AI will influence all facets of the health system. It is therefore essential that governments, organizations, caregivers, patients, and their families seriously explore and pursue the practical and ethical implications of AI-based solutions.

The literature on AI in health and medicine is wide, and therefore, the collection of documents reviewed in this essay is necessarily selective; however, it is representative of a range of disciplinary practices and theoretical perspectives. The primary objective of this essay is to discuss AI in health and medicine from a multidisciplinary perspective and to evince empirical, normative, and ethical issues as they emerge from an array of clinical and non-clinical contexts. A broad range of research outputs have been presented, including academic and professional publications and analytical articles. These span academic disciplines, including but not limited to: medical informatics, public health, public administration, law, anthropology, science and technology studies, media and communications, psychology, and gender studies. The material has been classified and is presented in relation to the empirical and normative dimensions of AI technologies in health and medicine, including but not limited to: the development, evaluation, and application of AI technologies; the structures, cultures, and contexts of AI technologies; technical capacities and theoretical frameworks; and social, ethical, policy, and legal issues. The review has revealed a number of issues, questions, and problems that are seen to emerge across a range of clinical contexts, including communities and wider society. In consolidating this extant literature, the next step is for work to address the complex interplay between empirical and normative streams that emerge across the papers.

3. Research Methodology

The overall aim of the research project is the investigation of the drivers and barriers surrounding the use of artificial intelligence in healthcare. The purpose of this section is to describe the method that will be employed and to provide a rationale for the chosen methodologies.

3.1 In-depth Semi-Structured Interviews The researcher will conduct approximately 20 semi-structured interviews with a range of healthcare professionals and IT specialists about the commercialization of innovation across different parts of the UK. These interviews will be transcribed verbatim to form a corpus of interview texts against which a range of multidisciplinary techniques, including linguistic analysis, will be applied in order to uncover findings.

3.2 Rationale for Research Approach The method of semi-structured interviews is a flexible research approach, allowing for a greater depth of understanding from those who review key aspects of the innovation process within the NHS. Given the lack of previous investigations within the area, there is a void of literature to help guide the development of the research design. In response to this, grounded theory is recommended as the guiding principle of the research. Grounded theory disregards the need for a comprehensive literature review in favor of developing theory from the data produced. While this is an advantage, it can make the process of data amalgamation and analysis more complex, as the researcher must continually reanalyze the data to ensure that no significant findings are overlooked.

4. Data Collection and Analysis

The data collection and analysis will provide an answer to the research questions pertaining to the impact of AI on healthcare. The procedures and data collection instruments were designed to allow for documenting the multiple perspectives on AI and healthcare based on a wide range of sources. Data collection is taking place by means of a combination of quantitative as well as qualitative approaches. This includes, for instance, literature analyses as well as online surveys or expert interviews in the context of the study. Against the background of the multidisciplinary approach, the data collection and analysis take a cross-disciplinary nexus systems approach to collecting and analyzing evidence. Particular attention is paid to capturing qualitative and quantitative evidence. This methodological approach is based on the understanding that the study problem cannot be adequately addressed solely within any single body of evidence and that no single policy or intervention will provide a sustained solution.

The methods used to collect and collate data include a systematic review of evidence as well as a comparative multidisciplinary analysis (CMDA). The evidence collated includes expert opinion and practitioner experience with the acceptance and adoption of AI evidence from other relevant domains, in combination with qualitative societal and public health studies, as well as an analysis of appropriate health and technological assessment evidence. The methods include a number of documented desk-based reviews of relevant literature and expert reports. Evidence will also be collected from a number of in-depth case studies that are designed to address specific themes or groups. Interviews are being undertaken with key stakeholders relevant to the research theme. Descriptive analysis is being used to analyze expert opinions and select cases and the qualitative data collected. The style of interviews is semi-structured, constantly following up and probing, to ensure the entire scope of identified topics and opinions are captured in order to create evidence-based findings.

5. Implications and Future Directions

How can the results of this study be used in the near future? The integration of artificial intelligence (AI) into healthcare and medical practice is an area of current interest. AI technologies will soon be used in this field, and through collaboration and multidisciplinary research such as this, we have engaged with the real-world questions that will arise. We identified the current and potential future concerns and main needs of all stakeholders. This knowledge can be acted upon, helping our AI and computer science colleagues develop technologies with which healthcare professionals and patients are more comfortable. Further, policymakers and healthcare organizations must plan for the integration of AI into healthcare services and repair the loss of trust from AI systems in the wider population and within specific communities.

The findings presented in this manuscript are of significance and relevance to the development of the existing research area and to those dealing with the implementation of optimized smart devices. Several practical implications exist for the healthcare community through the implementation of AI in healthcare. The study revealed the broad support for the recommendation of integrating AI systems in healthcare, and the encouraged movement towards AI for health research. Most interviewees and experts also agreed that the review is well-placed to consider issues from the healthcare perspective from the outset, such as framing the research questions from the perspective of potential end users (including healthcare professionals), patients, and policy of greater utility to our stakeholders, including healthcare professionals, patients, and the public. Given this broad-based support and the emphasis of the involvement groups on benefits to patients, we have retained the original focus on AI in the NHS in primary and community care. With the ongoing development of AI technologies, any differentials between primary and secondary care may become less relevant over time.