Literature review.. 5 Conceptual framework. 6 1. Data collection. 6 2. Data pre-processing. 6 3. Machine learning algorithms. 6 4. Clinical decision support 6 5. User interface. 7 6. Integration with existing healthcare systems. 7 Applications. 7 1. Medical diagnosis. 7 2. Predictive analytics. 7 3. Personalized treatment plans. 7 4. Medical research. 8 5. Remote patient monitoring. 8 Benefits. 8 1. Improved diagnostic accuracy. 8 2. Personalized treatment plans. 8 3. Increased productivity. 8 4. Predictive analytics. 8 5. Remote patient monitoring. 9 6. Medical research. 9 Limitations. 9 1. Limited data quality. 9 2. Lack of transparency. 9 3. Bias. 9 4. Regulatory challenges. 10 5. Legal and ethical concerns. 10 Literature gap. 10 Chapter summary. 11 Methodology. 12 1. Research design. 12 2. Data collection. 12 3. Data analysis. 13 4. Triangulation. 13 5. Ethics. 13 Research design. 13 Research type. 14 Data collection method. 15 1. Interviews. 15 2. Focus groups. 15 3. Observations. 15 4. Document analysis. 15 Sampling technique. 15 1. Purposive sampling. 15 2. Snowball sampling. 16 3. Convenience sampling. 16 Sample size. 16 Ethical consideration. 16 1. Informed consent 17 2. Anonymity and confidentiality. 17 3. Respect for participants’ autonomy. 17 4. Debriefing. 17 5. Ethical approval 18 Chapter summary. 18 References. 20 Literature review 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): 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): 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): 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 … Read more