Discussion: 
Machine learning can significantly improve healthcare; however, the downsides of machine AI need to be considered. Ethical dilemmas such as the potential of human biases when creating computer algorithms need to be addressed [34]. Health care predictions can vary by race, genetics, and gender amongst other variations, and failure to take these into account might over or underestimate patient risk factors. As stated in the review by Ho et al. concerning AI analytics in healthcare, it will become the responsibility of the clinician to ensure that AI algorithms are developed and applied appropriately [35]. It is imperative that healthcare continues to operate by ethically defined guidelines to sustain trustworthiness, and that medicine continues to prioritize the good of the patient. AI continues to be promising, as it can decrease healthcare costs and reduce clinician workload as it collaborates in decision making.
AI will become more intertwined in clinical practice, and there are machine learning instruments that no longer require the revision of a clinician to interpret their results, such is the case in IDX-DR AI device, which detects mild diabetic retinopathy [36]. Since this software does not require interpretation by a specialist, more primary care physicians can use it in their practice, potentially decreasing the workload for specialists and making the interpretation available for all types of clinicians. This can potentially be applied to Obstetrics regarding the interpretation of FHR and CTG interpretations.
There are instances where AI-based computer-aided design (CAD), has led to decreased diagnostic precision in the interpretation of mammography. AI alone was shown to be superior to a single radiologist in detecting breast cancer. However, in practice, an individual radiologist reviews these images, and due to bias might dismiss CAD suggestions [35]. However, when two specialists review the image, it is most likely to undergo additional testing if the readers disagree with each other. Thus, in this study, it was unclear if AI was cost-effective when compared to interpretation by two radiologists. Additional disadvantages are that AI-based CAD cannot explain the reasoning behind a decision. Thus, in case of a misinterpretation by the software, it is difficult to decide if wherever the manufacturer or the radiologist that interpreted the data is at fault. Thus, creating agencies that can develop standards that validate and ensure product quality and accuracy need to be established. Additionally, the algorithms created need to deliver under a broad range of settings that can adequately mirror real-world conditions to which they are being applied clinically [37]. Real-world settings are easier to mimic using a larger amount of data which can be obtained by accessing patient records, however, patient confidentiality becomes a challenge when retrieving personal information. The development of blockchain systems can potentially help to keep patient information confidential. This would allow the simultaneous sharing of data between centers, and incorporate it into the AI software, and allow it to continue expanding its array of records which would lead to improved accuracy [38].
Professionally, clinicians need to familiarize themselves with AI, to revise it so that the machine can provide accurate information. Furthermore, it needs to be flexible in adopting new information, and so the machine needs to continue learning and changing accordingly. Furthermore, the data must be representative of the population being evaluated in a realistic clinical setting. Despite challenges to AI, it has the overall potential of revolutionizing patient care by providing a more accurate diagnosis, alleviating the burden of work for clinicians, decreasing healthcare costs, and providing a baseline analysis in tests where substantial differences in interpretation between specialists exist. Further developments in medical AI will continue, and clinicians must embrace them, yet be wary, and when necessary, recognize its advantages and drawbacks to continue providing the best patient care.
Conclusions
AI helps to analyze medical data in disease prevention, diagnosis, patient monitoring, development of new protocols and assisting clinicians in dealing with voluminous data more accurately and efficiently. Further studies need to be done to decrease bias when creating algorithms and to increase adaptability in the system, enabling the incorporation of new medical knowledge as new technology surfaces. Practitioners must also take safety measures to ensure that the analysis is valid and accurate, AI is not meant to replace practitioners, but rather to serve as an adjunct in decision-making. Ethically, the use of patient records might bridge patient confidentiality since large amounts of data are required to enable AI systems to have access to the large and varied population statistics which are encountered in clinical settings, hence providing realistic and accurate predictions.
Authorship statement: Pulwasha Maria Iftikhar designed the study. Pulwasha M. Iftikhar, Marcela Kuijpers, and Aqsa Iftikhar performed the study, contributed to data extraction, literature review, analyzed the data, and wrote and proofread the manuscript. Other authors contributed to data results verification, manuscript proofreading and amendments. All authors provided critical feedback and helped to shape the research
Financial disclosure statement: This manuscript is original research, has not been previously published and has not been submitted for publication elsewhere while under consideration. Authors declare no conflict of interest with this manuscript. The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants, patents received, pending, or royalties.
Disclaimer: None
Conflict of interest: None
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