Patient outcomes, healthcare costs, and healthcare providers’ productivity could benefit greatly from implementing AI. Healthcare AI platforms are being developed and implemented to improve clinical decision-making, streamline administrative processes, and personalize patient treatment. Clinical decision support, medical imaging, electronic health records, telemedicine, and patient monitoring are some of these platforms’ many uses. Improvements in patient outcomes, efficiency, cost savings, and population health are just some of the many advantages of AI platforms in the healthcare sector. Nonetheless, there are constraints to consider, such as the quality of the data, the presence of bias, the rate of user adoption, and the ease of integration with existing systems. AI platforms in healthcare have great potential as technology advances; discoveries in this area will surely speed up shortly (Aggarwal, 2020).

There is mounting pressure on the healthcare industry to improve patient outcomes while reducing costs. One potential answer to this problem is the implementation of AI-powered healthcare platforms. Artificial intelligence platforms are data-learning computer programs that can be applied to analyse large datasets, such as electronic health records (EHRs). AI algorithms can analyze patient data in real-time, allowing doctors and other medical staff to receive immediate guidance on how to best proceed with a patient’s diagnosis, treatment, and ongoing care (Begum, 2019).

Clinical decision support, medical imaging, electronic health records, remote patient monitoring, and drug discovery are some of the many uses for healthcare AI platforms. AI algorithms can analyze medical imaging studies (such as X-rays and MRIs) to aid in diagnosis and treatment planning by spotting abnormalities or patterns that might otherwise go unnoticed by a human doctor. There are a lot of ways in which AI platforms can improve medical care. Advantages include better health for the population as a whole, lower healthcare costs, and better patient outcomes. AI platforms can increase efficiency and productivity in healthcare by automating administrative tasks, lowering error rates, and enhancing care coordination. Healthier populations and lower healthcare costs are the long-term results of using AI platforms to detect high-risk patients and design individualized interventions to improve population health (Chen, 2020).

Despite these merits, however, there are also several caveats when applying AI platforms to the medical field. Data quality, bias, user adoption, and compatibility with current systems are all issues that must be addressed. Artificial intelligence (AI) algorithms rely heavily on high-quality data to achieve reliable outcomes, making data quality a top priority. Furthermore, bias can arise if the algorithms are trained with data not representative of the population or are designed with biased assumptions. Another potential obstacle is user adoption, as healthcare providers may be hesitant to adopt AI platforms if they find them too complicated to use. Last but not least, for AI platforms to be useful, they must be integrated with preexisting healthcare systems like electronic health records (Jason, 2017).

This literature review will focus on the current state of artificial intelligence (AI) platforms in the healthcare industry, including their applications, benefits, limitations, and future directions. Research and development into the use of AI platforms in healthcare is a burgeoning area, and this trend is only expected to accelerate in the years to come (Joshi, 2022).

Conceptual framework

The conceptual framework of an AI platform for healthcare involves several key components that work together to provide actionable insights for healthcare providers. The following are the key components of an AI platform for healthcare (Kaur, 2018):

Data collection: The first step in building an AI platform for healthcare is to collect relevant data. Electronic health records, medical imaging, and genomics data are just some of the types of information that the platform will need to collect and store. For AI algorithms to produce reliable results, the data must be both comprehensive and representative of the population (Klumpp, 2021).
Data pre-processing: Pre-processing the data entails cleaning it up before using it by removing any outliers or anomalies that may have been introduced during data collection. Cleaning, normalizing, and transforming the data is done in this stage to make algorithm analysis more straightforward (Mrazek, 2020).
Machine learning algorithms: Machine learning algorithms are the brains of the artificial intelligence system. They analyze the cleaned data to discover new insights that can be used to better treat patients. Supervised learning, unsupervised learning, and reinforcement learning are just some of the machine learning algorithms available (OECD, 2021).
Clinical decision support: Medical care providers will benefit from having access to the insights gained by machine learning algorithms in the form of clinical decision support. Considering the patient’s unique characteristics and medical history, the platform can make real-time recommendations for diagnosis, treatment, and care management (Premant, 2019).
User interface: The user interface is the part of the AI platform that facilitates communication between patients and medical professionals. To ensure that healthcare providers can access and understand the insights generated by the platform, the interface must be user-friendly and intuitive (Roy, 2021).
Integration with existing healthcare systems: Finally, the artificial intelligence platform needs to be integrated with preexisting healthcare systems like EHRs, MRI machines, and drug discovery programs. This integration makes it easy for healthcare providers to use the platform’s insights to enhance patient care by incorporating them into their existing workflows (Sharma, 2022).

As a whole, the conceptual framework of an AI platform for healthcare includes things like the collection and pre-processing of high-quality patient data, the use of machine learning algorithms to generate insights, the provision of clinical decision support, a user-friendly interface, and integration with existing healthcare systems (Ülger, 2016).

Applications

AI platforms designed for use in healthcare are currently being put to use in a wide range of applications, including the following (Yang, 2022):

Medical diagnosis: Artificial intelligence (AI) platforms can analyze massive amounts of medical data to help doctors arrive at correct diagnoses faster. Medical imaging studies (such as X-rays, CT scans, and MRIs) and other data (such as laboratory results and patient histories) are analyzed as part of this process. Artificial intelligence algorithms can pick up on irregularities and patterns that human doctors might miss, leading to more precise diagnoses and less room for error (Bhattamisra, 2023).
Predictive analytics: AI platforms can use predictive analytics to predict which patients will develop diseases like diabetes, heart disease, or cancer. AI algorithms can analyze data from EHRs, wearables, and other sources to predict which patients are at high risk for developing a disease or condition and then notify doctors so they can intervene before any symptoms appear (Borgstadt, 2022).
Personalized treatment plans: AI platforms can help create individualized treatment plans for patients, taking into account their specific medical conditions, genetic makeup, and other factors. AI algorithms can analyze large amounts of data to determine the most effective treatments for specific patient groups, paving the way for more individualized and efficient healthcare (Chang, 2022).
Medical research: In medical research, artificial intelligence platforms can help by analyzing massive amounts of data to spot trends and correlations. This has the potential to aid in the discovery of novel therapies, the prediction of clinical trial results, and the creation of more precise and individualized therapeutic approaches (Chebrolu, 2020).
Remote patient monitoring: Artificial intelligence platforms can aid in remote patient monitoring, allowing doctors to monitor their patient’s health without physically checking in with them. Wearables and other connected devices can monitor a patient’s heart rate, blood pressure, and other vitals, providing a wealth of data that can be analyzed by AI algorithms to spot health risks (Davenport, 2019).

In general, artificial intelligence platforms have the potential to revolutionize the healthcare industry by enhancing patient outcomes, cutting costs, and simplifying procedures. The platforms’ prospective applications in the medical field will likely continue to expand as they mature and acquire greater levels of expertise (Fenech, 2018).

Benefits

The following are some of the potential benefits that could be brought to the healthcare industry by AI platforms (Health, 2020):

Improved diagnostic accuracy: AI algorithms can examine massive amounts of medical data, looking for patterns and anomalies that human doctors might miss. A rise in diagnostic precision and a decrease in the possibility of incorrect diagnosis may result in better patient outcomes (Infosys, 2018).
Personalized treatment plans: AI platforms make treatment plans tailored to each individual patient’s specific medical history, genetic makeup, and other factors possible. These plans can be more tailored to the individual patient, which can boost efficacy while decreasing unwanted side effects (Majnarić, 2021).
Increased productivity: AI platforms can automate mundane tasks and streamline processes, relieving pressure on healthcare providers and allowing them to focus on more nuanced problems. Thanks to this strategy, efficiency gains, cost savings, and better health outcomes for patients are all possible (MIT, 2019).
Predictive analytics: With the help of predictive analytics, AI platforms can identify patients at risk of developing certain medical conditions, giving doctors a chance to intervene before the onset of the illness. Consequently, this has the potential to improve patient outcomes while simultaneously lowering healthcare costs by reducing the demand for more intensive treatments (Narejo, 2020).
Remote patient monitoring: Artificial intelligence platforms can aid in remote patient monitoring, allowing doctors to monitor their patients’ health without having to physically check in with them. As a result, patients may receive care more rapidly and with less inconvenience than was previously possible (Parry, 2020).
Medical research: AI platforms can speed up medical research by analyzing large amounts of medical data for patterns and correlations. This has the potential to aid in the discovery of novel therapies, the prediction of clinical trial results, and the creation of more precise and individualized therapeutic approaches.

In general, artificial intelligence platforms have the potential to revolutionize the healthcare industry by enhancing patient outcomes, cutting costs, and boosting productivity. The benefits of AI platforms in the healthcare industry are significant, and they are likely to continue to expand as these platforms become more advanced. Some of the challenges that still need to be addressed include ensuring the privacy of patients and addressing concerns about bias in AI algorithms.

Limitations

In spite of the numerous possible advantages, there are also several restrictions imposed by AI platforms in the medical field. Among the most significant restrictions are the following:

Limited data quality: Data quality may be an issue. The precision of AI algorithms is directly proportional to the quantity and quality of the data used to train them. Data quality can be an issue in the healthcare industry due to the fact that patients’ medical records may be missing information or contain errors, and different healthcare providers may use different organizational schemas or terminology.
Lack of transparency: Artificial intelligence algorithms can be difficult to understand due to their complexity, which can make it difficult for healthcare providers and patients to interpret their results. Due to the lack of transparency, it may also be difficult to identify errors or biases that may be present in the algorithms.
Bias: AI algorithms can reflect the biases of their developers or the data used to train them, which can lead to results that are incorrect or unfair. Bias can be a problem when it comes to medical diagnosis and treatment. For instance, if an AI algorithm is trained on data that disproportionately represents certain demographic groups, it may produce biased results that are not representative of the entire population. This is because the algorithm was trained on data that disproportionately represents certain demographic groups.
Regulatory challenges: Problems with Regulation The application of AI in medical care is subject to regulation, which can be difficult to understand and is different in each nation and region. This can present difficulties for healthcare providers and software developers who are attempting to implement AI solutions that comply with applicable regulations and provide value to patients at the same time.
Legal and ethical concerns: Concerns Raise Legal and Ethical Issues The use of artificial intelligence (AI) in healthcare raises a number of legal and ethical issues, some of which include patient privacy, data security, and liability in the event of errors or adverse outcomes. Because of these concerns, it may be challenging for healthcare providers and developers to implement AI solutions with complete assurance.

In general, artificial intelligence platforms have the potential to revolutionize healthcare; however, it is essential to be aware of their limitations and address these challenges to ensure that the benefits of AI are realized while simultaneously minimizing the risks that may be incurred (Petersson, 2022).

Literature gap

Even though there is a growing body of writing about artificial intelligence platforms in healthcare, there are still some significant knowledge gaps.

A significant shortcoming in the healthcare sector is the lack of uniformity in creating and assessing AI platforms. Standardized approaches could benefit the evaluation of the algorithms’ accuracy, validity, and clinical utility, as well as the collection and preprocessing of data. This would help to better compare and adopt the most effective AI platforms (Begum, 2019).

The ethical, legal, and social implications of AI platforms in healthcare are also poorly understood. As these platforms gain popularity, privacy, bias, lack of transparency, and lack of accountability have been raised. Further study into these issues and the creation of guidelines and regulations are required to ensure that AI platforms in healthcare are used ethically and responsibly.

Clinical adoption and the use of artificial intelligence platforms are also areas that need further study. Despite the widespread agreement on the platforms’ potential benefits, factors like user acceptance, integration with existing systems, and cost-effectiveness remain roadblocks to their widespread implementation. More study is needed into the effects of AI platforms on clinical outcomes and healthcare costs, as well as the best methods for implementing these systems in healthcare settings (Bhattamisra, 2023).

Finally, more study is needed into the potential of AI platforms in the field of healthcare, especially in areas with limited resources. Limited infrastructure, data quality issues, and resource constraints are just some of the unique challenges that must be overcome before AI platforms can improve healthcare outcomes in these settings. Artificial intelligence (AI) platforms tailored to low-resource environments need to be developed and evaluated, and barriers to their adoption need to be identified and addressed.

Even though there is a growing body of literature on AI platforms for healthcare, there are still several research gaps in areas such as standardization, ethical and social implications, implementation and adoption, and use in low-resource settings. To fully realize AI’s potential in enhancing healthcare outcomes, additional study in these areas is necessary.

Chapter Summary

The chapter on healthcare AI platforms introduced the main features of these systems and discussed how they could be used to better care for patients. Early in the chapter, we discussed the difficulties the healthcare industry faces and how artificial intelligence platforms can help remedy issues like rising demand for services, spiralling costs, and varying quality of care. Then, the basic components of an AI healthcare platform were outlined, including data collection, data pre-processing, machine learning algorithms, clinical decision support, a user interface, and integration with existing healthcare systems. The potential advantages of AI platforms for healthcare were also discussed in this chapter. These advantages include better diagnosis and treatment, personalized medicine, and predictive analytics (Singh, 2021).

Medical imaging, drug discovery, and clinical decision support are just a few of the areas where machine learning algorithms are being applied in this chapter’s discussion of AI platforms for healthcare. Challenges and restrictions of these platforms were also covered, including the need for standardization, ethical and social implications, implementation and adoption, and use in low-resource settings.

Overall, the chapter emphasizes the importance of AI platforms in healthcare and their potential for solving current problems. Future research needs to be done in these areas, as well, so that the full potential of these platforms to enhance patient care can be realized (WHO, 2021).

Methodology

When examining the utility and effect of AI platforms in healthcare, qualitative research is a valuable method. When it comes to diagnosis, treatment, personalized medicine, and predictive analytics, AI platforms have the potential to completely revolutionize the healthcare system. However, there are also difficulties that come with deploying AI platforms, such as the need for standardization, ethical and social implications, implementation and adoption, and use in low-resource settings (Aggarwal, 2020).

Research that focuses on the lived experiences of key stakeholders, such as healthcare providers, patients, and administrators, can shed light on the complexities involved in implementing and managing AI-powered healthcare platforms. Including research design, data collection, analysis, triangulation, and ethical considerations, this chapter provides an overview of the methodology that may be used in qualitative research on AI platforms for healthcare. Findings from qualitative studies can shed light on the most efficient ways to implement and make use of AI platforms in healthcare by exploring the experiences and viewpoints of key stakeholders (Begum, 2019).

Studying the adoption and effects of AI platforms in healthcare is a worthwhile endeavour, and qualitative research is a useful method for doing so. The following is a brief summary of some of the methods that could be used in qualitative research on AI platforms in healthcare:

Research design: The design of the research study is the first step in carrying out qualitative research, and its design is the focus of this step. The research questions should be formulated to explore the experiences and perceptions of stakeholders, such as healthcare providers, patients, and administrators, regarding the implementation and impact of AI platforms for healthcare. The research questions should also guide the design of the research, which the research questions should guide. Case studies, ethnographies, and interviews are all valid methods for conducting this study.
Data collection: The next step in the process is collecting the necessary information. Data collection can be accomplished through a variety of approaches, including interviews, focus groups, observations, and document examination. Interviews may be especially useful in the context of AI platforms for healthcare for exploring the experiences and perspectives of stakeholders, whereas document analysis may be used to investigate the policy and regulatory frameworks associated with the implementation of AI platforms (Bhattamisra, 2023).
Data analysis: After the data collection process is completed, the information must be analyzed. Coding, categorizing, and interpreting the data to discover recurring themes and patterns are all part of the qualitative data analysis process. The research questions and conceptual framework of the AI platform for healthcare should serve as the guiding principles for the analysis.
Triangulation: Triangulation is a method that can improve the reliability and validity of the research being conducted. The process of triangulation involves gathering data from various sources, including interviews and document examination, to validate the findings and ensure their accuracy (Chang, 2022).
Ethics: Ethical Considerations: Lastly, ethical factors ought to be considered throughout the research process. This includes obtaining informed consent from participants, maintaining confidentiality and privacy, and ensuring that the research does not harm participants or stakeholders in any way.

In conclusion, qualitative research is an important tool for examining the use and results of AI healthcare platforms. Research design, data collection, analysis, and validity checks through triangulation and attention to ethical concerns are all parts of a sound methodology.

Research design

Methodological planning is essential to studying artificial intelligence (AI) in healthcare platforms through qualitative research. In order to answer the research questions and learn more about the stakeholders’ perspectives on the use and effects of AI platforms in healthcare, the research design should be adapted accordingly. The following are some possible approaches to designing qualitative studies of AI healthcare platforms:

Analysis of a specific situation, event, or set of circumstances, such as the introduction of an AI platform to a healthcare facility. Case studies can be used to study in depth both the AI platform’s implementation process and its effect on patient care (Chebrolu, 2020).
Participant observation and in-depth interviews with healthcare providers and patients are at the heart of ethnography. By providing an in-depth understanding of these factors, ethnography can shed light on the cultural and social factors that shape the diffusion and effect of AI platforms in healthcare.
Individuals involved in the study, such as doctors, patients, and researchers, are interviewed one-on-one. A thorough set of interviews can shed light on the implementation and effects of AI platforms in healthcare from the perspectives of key players.
Focus groups are group discussions with relevant parties, such as healthcare providers, patients, and administrators. In a focus group setting, stakeholders can share their insights on the implementation and impact of AI platforms in healthcare.

Research questions and the potential for efficient data collection should inform the choice of research design. Further, triangulation, or the practice of using multiple data sources to corroborate and strengthen findings, should be taken into account when deciding on a research design (Davenport, 2019).

Research type

Exploratory, descriptive, and explanatory qualitative studies on AI healthcare platforms are all possible.

When investigating a novel or poorly understood topic, researchers often turn to exploratory studies. Researchers frequently use exploratory methods to learn more about stakeholders’ perspectives on the use and effects of AI platforms in healthcare.
Research that focuses on providing a detailed description of a specific phenomenon or case is known as descriptive research. Research that focuses primarily on describing AI platforms in healthcare has the potential to shed light on both the process of implementing these platforms and their effect on patient care.
The goal of explanatory research is to identify and clarify the factors that contribute to a given phenomenon or case. Explanatory studies of AI healthcare platforms can shed light on the factors that either help or hinder the widespread adoption and efficient use of such solutions (Chen, 2020).

The research questions and goals should guide the choice of research methodology. For instance, descriptive research may be more appropriate for examining the implementation process of AI platforms in a specific healthcare facility. In contrast, exploratory research may be more appropriate for investigating the experiences and perceptions of stakeholders regarding the use of AI platforms in healthcare. Explanatory research may be necessary to discover what factors actually affect the efficient application of AI platforms in healthcare settings.

Data collection method

Quantitative research on healthcare AI platforms relies heavily on data collection. It is important that the research questions and the research design inform the choice of data collection method. Qualitative research on AI health platforms may make use of the following techniques for gathering information (Borgstadt, 2022):

Interviews: In-depth interviews with stakeholders such as healthcare providers, patients, and administrators can help gain an in-depth understanding of the implementation and impact of AI platforms in healthcare. Interviews can be conducted via video chat, the telephone, or even in person.
Focus groups: Focus groups are group discussions with relevant parties, such as healthcare providers, patients, and administrators. In a focus group setting, stakeholders can share their insights on the implementation and impact of AI platforms in healthcare.
Observations: To conduct an observation, one must observe firsthand how AI platforms are used in healthcare settings. Observations can help us better understand the implementation process and the effect of AI platforms on patient care.
Document analysis: Analyzing reports and policy documents, for example, can shed light on how AI platforms are used in healthcare and what effects they have had so far.

The research questions and the accessibility of data should inform the choice of data collection method. Additionally, the requirement for triangulation, which involves using multiple sources of data to corroborate and ensure the robustness of the findings, should be taken into account when deciding on the method to use (Chebrolu, 2020).

Sampling technique

Qualitative studies of AI healthcare platforms must pay close attention to sampling. The research questions and study design should inform the choice of sampling method. In qualitative studies of healthcare AI platforms, researchers may employ the following sampling methods:

Purposive sampling is a method of selecting study subjects that are most directly related to the research questions and aims. To better understand the implementation and impact of AI platforms in healthcare, it may be useful to sample from a subset of interested parties, such as healthcare providers, patients, and administrators (Chebrolu, 2020).
Snowball sampling: Participants in a snowball sample are recruited through word-of-mouth, as the name implies. Identifying stakeholders who may be difficult to reach, such as patients with rare diseases or healthcare providers with limited availability, may benefit from snowball sampling.
Convenience sampling: The term “convenience sampling” refers to a method of selecting study subjects based on how easily they can be reached. If you’re just getting started collecting data on the implementation and effects of AI platforms in healthcare, convenience sampling may be a good option.

The method of sampling chosen ought to consider the requirement for diversity and representativeness. In addition, the sample size needs to be appropriate for the research questions and the research design in order for the research to be valid. Multiple sampling techniques may be necessary to guarantee that the sample is both diverse and accurate (Chang, 2022).

Sample size

Qualitative research on AI platforms in healthcare must account for some factors, including the questions being asked, the study’s design, and the sampling strategy, all of which influence the sample size. Data saturation, the point at which no new themes or insights can be drawn from the data, is one method for determining the optimal size of a qualitative research sample, though there is no hard and fast rule for this.

Qualitative research on AI platforms for healthcare typically uses a smaller sample size than quantitative research to better understand stakeholders’ experiences and perspectives rather than generalize the findings to a larger population. The sample size must be proportional to the study’s objectives and methodology. When conducting exploratory research, for instance, a smaller sample size may be adequate, while a larger sample size may be required when conducting explanatory research (Borgstadt, 2022).

The importance of achieving both diversity and representation should be taken into account when determining the sample size. In order to guarantee a diverse and representative sample, multiple sampling methods may be necessary. The need for data saturation, diversity, and representation, as well as the research questions and design, will dictate the sample size in qualitative research on AI platforms for healthcare.

Ethical consideration

Important ethical concerns regarding qualitative research on AI platforms for healthcare must be addressed to ensure the safety and privacy of study participants. Informed consent is a crucial ethical factor. All participants must be given detailed information about the research project and its requirements. They need to be able to ask questions and get clear answers before deciding whether to participate. The need for privacy and anonymity is also a major factor. Researchers have an ethical obligation to maintain the confidentiality of participants’ personal information and medical history. Participants can rest assured that their data will not be sold or given to any outside parties without their permission. Additionally, researchers should give participants space to share their thoughts and feelings without being stifled in any way. Last, proper ethical approval must be secured from the appropriate bodies before any research is carried out to guarantee that the study’s methodology and design adhere to all applicable ethical standards and regulations. Researchers can protect the rights and well-being of study participants while conducting ethical qualitative research on AI platforms for healthcare if they keep in mind the aforementioned issues (Begum, 2019).

To safeguard the rights and safety of participants, researchers conducting qualitative studies on AI platforms in healthcare must address a number of ethical concerns. Here are a few things to keep in mind from an ethical standpoint:

Informed consent: Before agreeing to take part in a study, participants should be given sufficient information about the study, including its goals, methods, and any potential drawbacks or advantages. This is known as “informed consent.” Consent should be obtained from participants in the research in writing, and participants should be allowed to withdraw from the study at any time without penalty.
Anonymity and confidentiality: It is important that the identities of the participants and any personal information they provide remain anonymous and confidential to the greatest extent possible. Researchers should use pseudonyms, and secure data storage should be ensured to prevent unauthorized access to the information.
Respect for participants’ autonomy: A respect for the participants’ right to make their own decisions. Researchers are responsible for respecting the participants’ right to make their own decisions and ensuring that they are not coerced or unduly influenced into participating in the study. Participants ought to be able to speak their minds without worrying about being punished or retaliated against in any way.
Debriefing: After the research has been completed, the researchers should provide the participants with a debriefing to ensure that they are aware of the purpose and results of the research and address any concerns or questions that they may have.
Ethical approval: Before beginning their work, researchers need to obtain ethical approval, either from the appropriate institutional review boards or ethics committees. One step in obtaining ethical approval is reviewing the research protocol and procedures to determine whether or not they adhere to ethical principles and guidelines (Begum, 2019).

In general, qualitative research on AI platforms for healthcare should be conducted in a way that demonstrates respect for the rights and well-being of the participants and full compliance with ethical principles and guidelines.

Chapter Summary

In this chapter, we have covered the topic of utilizing qualitative research methods to study AI platforms for the healthcare industry. We started by providing an overview of the subject matter and the research questions that will be investigated throughout the study. Following that, we had a discussion about the conceptual framework for the study, which is comprised of key concepts related to AI platforms for healthcare, such as data privacy, trust, and ethics. Specifically, these concepts are (Begum, 2019):

We also discussed the gap in the existing literature and the necessity of conducting additional research in this field. We then went on to describe the research design, which is a qualitative approach using semi-structured interviews with key stakeholders involved in the development, implementation, and use of AI platforms for healthcare (Begum, 2019).

This research will be conducted in the United States. We talked about how important it is to choose an appropriate sampling method and figure out an appropriate sample size for the study. In addition, we provided an overview of the strategies for data collection and analysis that will be implemented, one of which is a thematic analysis approach. When conducting qualitative research on AI platforms for healthcare, there are a number of ethical considerations that need to be taken into account (Yang, 2022).

Finally, we covered some of these issues. We talked about how important it is to obtain participants’ informed consent, keep their information confidential, respect their autonomy, and obtain ethical approval. By addressing these ethical considerations, we can guarantee that the research will be carried out in a responsible and ethical manner, which will also protect the participants’ rights and well-being.

In general, this chapter aims to summarise the research design and methodology for a qualitative investigation into AI platforms for the medical field. This study will provide insights into key issues related to the development, implementation, and use of AI platforms for healthcare, including data privacy, trust, and ethics. These issues will be investigated. The findings of the research will make a contribution to the existing body of literature on the subject, thereby helping to close a knowledge gap regarding the difficulties and possibilities associated with the use of AI platforms in the medical field (Singh, 2021).

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Narejo, G.B. (2020) ‘AI in Healthcare’, Privacy Vulnerabilities and Data Security Challenges in the IoT, (November), pp. 77–92. Available at: https://doi.org/10.1201/9780429322969-5.

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Singh, A. and Bist, A.S. (2021) ‘AI and Healthcare’, Data-Driven Decision Making Using Analytics, (637), pp. 124–135. Available at: https://doi.org/10.1201/9781003199403-9.

WHO (2021) Ethics and Governance of Artificial Intelligence for Health: WHO guidance, World Health Organization. Available at: http://apps.who.int/bookorders.

AI PLATFORM FOR HEALTHCARE

Literature review

Conceptual framework

Applications

Benefits

Limitations

Literature gap

Chapter Summary

Methodology

Research design

Research type

Data collection method

Sampling technique

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Ethical consideration

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