Are ethical considerations a barrier to using Machine Learning Techniques in Healthcare? (Research Paper Sample)

ARE ETHICAL CONSIDERATIONS A BARRIER TO USING MACHINE LEARNING TECHNIQUES IN HEALTHCARE By (Name) Tutor Course Institution Location of Institution Date Research Questions Are ethical considerations a barrier to using Machine Learning Techniques in Healthcare? Should ethical considerations stop them from using Machine Learning Techniques in Heart diseases? Are ethical considerations a barrier to using Machine Learning Techniques in Healthcare Introduction Machine learning (ML) entails using data and algorithms to imitate how people learn and gradually enhance accuracy. The components of the ML algorithm include the error function, decision process, and model optimization process. Error function may be integral in evaluating the model's accuracy through comparison. Michelson et al. (2022, 125) reiterate that it helps assess the model's prediction. The ML algorithm can help make predictions and classifications concerning the decision process. Davenport and Kalakota (2019, 94) assert that the algorithms can help estimate patterns in vast data depending on input data. As for the model optimization process, the algorithms are vital in reviewing the misses and updating the methods for making the final decision. It is prudent to provide insights about the basis of ML in healthcare within the context of electronic health records. Shreve et al. (2022, 842) explain that electronic health records (EHRs) and EMRs (Electronic medical records), when available, provide the basis for the integration of the algorithms of ML into medical applications. Companies in developed nations have vast collections of electronic health records. There is a possibility that (ML) machine learning may replace physicians in healthcare, particularly on issues such as radiology and anatomical pathology. Rasheed et al. (2022, 106043) explain that experience with machine learning tools is likely vital for the next generation to examine big data. Reddy et al. (2020, 491) illuminate that companies, especially in the private sector, increasingly embrace machine learning in their healthcare and medical decision-making. However, scientific studies have yet to manage to proportionately address ethical challenges related to machine learning (ML) use. Things began changing for the better recently. Aung et al. (2021, 6) elaborate that healthcare organizations should realize the benefits of machine learning and consider the ethical challenges perceived as inherent in ML tools. Patel & Shah (2022, 134) add that challenges appear straightforward while others seem to have less apparent risks. These lead to much greater concerns, like how algorithms can become the repository associated with the collective mind of medicine. Methodology The research used published literature sources (secondary data) from different databases to collect data on issues related to ethical considerations as the barrier to using Machine Learning Techniques in Healthcare. Using literature sources entailed collecting information and data from published texts and write-ups that were available and accessible in the public domain. The research focused on using peer review publications in its data collection technique. Peer reviews emanated from the understanding that they were considered the cornerstones of scientific journals. They were publications screened before the new knowledge they sought to convey had been diffused. Peer reviews were vital in evaluating research publications' relevance, quality, significance, and validity. Consequently, it helped in maintaining the credibility not only of their publications but also of their study field. Abd Rahman et al. (2020, 183952) explain that good peer reviews have professionalism, expertise, and critical skill. They served as screens before information and data were published; researchers considered them to improve submitted research work. The secondary sources used emanated from research databases such as PubMed, Scopus, Web of Science, ScienceDirect, BioMed Central (BMC), and CINAHL Complete. Some of the terms used for searching for sources included the application of ML on heart diseases, the impact of ML on healthcare, and the development of machine learning technology on healthcare issues. Other search terms included prediction models using ML, qualitative research, and cardiovascular diseases. The words were formatted for searching before turning them into searches. Filters and limits were also utilized in the databases for refining the searchers, after which the search results were reviewed. Systematic reviews of qualitative research about machine learning and its application in healthcare and heart diseases were conducted through the abovementioned databases. Those that met the inclusion criteria were incorporated. References to these systemic reviews were subsequently hand searched from the above databases. Restrictions to the publication were applied to range from 2019 to 2023. Systematic reviews systematically evaluated full text per the exclusion and inclusion criteria. The inclusion criteria entailed the systemic reviews of qualitative and quantitative research about the impacts of ML on all types of heart diseases. Results The search strategy on the databases resulted in 2060 articles using search queries. Duplicates were eliminated, and 1645 articles were screened. Further screening of the articles led to the exclusion of 87%. Screening the full text of the remaining research articles led to 190 articles being excluded for varied reasons based on predefined criteria. Eight articles were selected for data charting. The table below demonstrates the collection of the articles' findings on issues considered ethical barriers. The table maps the outcomes of the ethical principles that have been measured through the number of studies in the scoping review. The mapping involved measuring several studies that examined the application of machine learning in healthcare within the context of heart diseases. The table demonstrates the collection of the articles' findings Articles Percentage Patient data privacy 36% Fairness 21% Accountability 24% Transparency 19% The findings of the sources used in the research revealed that accountability, fairness, privacy, and transparency are some of the ethical concerns that acted as barriers to using Machine Learning Techniques in Healthcare. 24% of the articles focused on accountability. They demonstrated that machine learning algorithms had resulted in new situations and circumstances. For instance, the actor of the ML pipeline may not be able to predict future machine actions. As a result, actors cannot be held liable and ethically responsible. It is vital to note that some mistakes can emanate from machine learning algorithms. Large models of language usually hallucinate but periodically. It can provide factually incorrect and provides irrelevant answers because of the need for sufficient information. For example, generative AI (artificial intelligence) continues to have limitations, such as social biases and hallucinations. Technology firms should work towards addressing these issues. However, these risks are still high not only for medical use but also for patient care settings. For example, according to Michelson et al. (2022, 126), there is a need for improvement for health organizations to conduct ethical machine-learning practices. It can be achieved through adequate enforcement techniques. The assertion stems from the understanding that additional legislation should be incorporated to help regulate these practices. The negative ethical implications that may emanate from the unethical use of machine learning systems can be the only motivation and incentive for organizations to remain ethical. The findings revealed that integrating responsibility with the absence of proactivity with potential consequences or repercussions should not be considered effective in addressing societal damage. Secondly, Shreve et al. (2022, 842) explain that increasing machine-learning applications can result in governments looking for suitable solutions to handle the ensuing legal gaps. Regarding fairness, it entails examining different principles and navigating complex issues. Ensuring these impartial treatments consists of the evaluation of outcomes and processes. It not only allocates costs but also gains. It does so while also avoiding unfair bias and arbitrary decisions. Ethical questions about the utilization of machine learning have stemmed from the discrimination and bias cases related to machine learning systems. As a result, there needs to be safeguards against them, particularly when the data for training becomes the product associated with biased human processes. Biases emanate from problems with algorithmic design for human perception and decision-making. The findings highlighted that 36% of the articles considered patient data privacy as an ethical concern, while fairness and transparency constituted 21% and 19%, respectively. Privacy issues entailed issues related to both data security and data protection. Challenges associated with data privacy emanated from electronic healthcare records (EHRs). The data utilized for models of training is a privacy concern. The ability of systems to obtain data and information can result in the violation of privacy. It does so by retrieving information without consent and scrapping individual or personal information. In addition, many models of language can lead to the leakage of personal data. The converse also holds because reverse engineering, including inference style, may lead to de-anonymizing training data. Consequently, it compromises the legitimacy of the data that has been collected. On the other hand, the articles revealed that transparency was integral in ensuring that stakeholders could access information and data required for ML decision-making. It constitutes issues such as traceabil...