Artificial Intelligence in Healthcare (Research Paper Sample)

NAME INSTITUTION COURSE NAME OF THE CLASS INSTRUCTOR DATE ARTIFICIAL INTELLIGENCE IN HEALTHCARE ARTIFICIAL INTELLIGENCE IN HEALTHCARE: The opportunities and risks of using Medical Diagnostics. INTRODUCTION. Artificial Intelligence also refers to intelligence in machines that has the prospect of revolutionizing several industries, including healthcare. For instance, the application of AI in the diagnosis of diseases has proved to be of great value to the practice of medicine as it enhances the efficiency and reliability of the healthcare delivery. By using machine methodologies and having access to vast amounts of medical data AI can analyze the data, look for patterns and provide diagnostic perspectives that even the best physicians cannot give. However, as these forecasts are inspiring, the use of AI is making medical diagnoses also entails certain problems. These are issues such as ethical questions and questions regarding the privacy of the data and questions that regard the necessity of ensuring the validation and regulation of the AI technologies. This paper explores all the possible avenues that AL can offer to the medical field with regard to diagnostic capability of diseases and also the challenges that need to be met so as to realize this possibility. AL IN HEALTHCARE AND APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN MEDICAL DIAGNOSTICS. IMPROVING DIAGNOSTIC ACCURACY. The key bound of the advancement of AI is in the extreme improvement of the diagnostic precision by delivering exceptional capacity to analyze medical data. HL can be learned with vast and diverse data sources such as medical imaging data, patient electronic health records, and genomic data where it may be difficult for clinics to find patterns and connections. For instance, within the application of radiology, the AL has evaluated signs of diseases such as cancer at their initial stages. For example, breast cancer screening, it has emerged that accuracy of an AI algorithm in detecting malignant tumors in mammograms is higher than that of radiologists. This potentially cut down the amount of false positives and false negatives that are currently seen in diagnostic tests. (McKinney et al., 2020). ENABLING PERSONALIZED MEDICINE. Thus, AI is also behind the concept of medicine, a relatively new practice of treatments, whereby the custom from the patient determines the treatment plan. This is quite easy to do by looking at the patient’s genes, or even their environment or decisions in general, and then being able to calculate how they will most likely react to certain types of treatment. And this makes the treatment to more efficient and specific. For example, in the field of oncology, data science tools can access the patient’s gene data to look for mutations that would likely contain specific drugs and the effectiveness of treatment independently of multiple underlying tractions that lead only to overtreatment and poor results (Topol, 2019). STREAMLINING DATA PROCESSING The healthcare industry in particular generates large quantities of data every day; ranging from patients’ records to images and research data. Healthcare data can be effectively managed by AI systems and the output is available for perusal by healthcare professionals within a very short time. For example, clinical decision support systems can contain AI that is capable of analyzing patient data to make diagnoses as well as recommend treatments and expected patients’ outcomes. This can make generation of diagnosis easier, reduce effort from the health care professionals and thus improve the patient outcomes (Reddy et al., 2020). FACILITATING EARLY DISEASE DETECTION Another improvisation of AI in medical diagnosis is early ailment and this is attributed to the fact that early treatment has always been effective. AI systems can detect the onset of early-stage diseases such as Alzheimer’s, Parkinson’s or cardiovascular diseases on medical images or biomarkers that the patient may display. Thanks to deep learning, diseases can identify at an earlier stage, whereas the required interventions may delay the further development of the disease and improve the patient’s quality of life (Esteva et al., 2017). THE PRIMARY CONTROVERSY IN APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF HUMAN DISEASES. CURRENT ISSUES: DATA PRIVACY AND SECURITY. AI in medical Diagnostics also depends on the availability of big data and this comes with the patient’s information. Ensuring privacy and security of this data is very difficult feat. Medical data as a data type, has to adhere to strict guidelines for its collection. Storage, and sharing especially those set in USA under the health insurance portability and accountability Act (HIPAA) and in EU under the general data protection regulation (GDPR). As with other information technology tool, there remains the potential of hacking or other forms of illegitimate intrusions which would compromise patient information and erode their confidence in the use of such AI systems (Larsson et al., 2020). MINIMISING BIAS AND MAKING FAIRNESS The current state of knowledge of AI algorithms is that the algorithms shall be as good as the data they are trained on. If the training data set is not reliable or it’s a biased data set, the AI system will be prejudiced or wash out a particular population at that. For example, it has also been noted that if t he AI model was trained on the data of and from one particular demographic, then it would work less efficiently for patients from the demographic. This might result in inequalities in the diagnosis and treatment given to patients and is therefore an issue of concern. Bias in AI systems must be resolved in order to prevent discriminations in the medical diagnostics among different groups of patients (Char et al., 2018). VALIDATION AND REGULATION CHALLENGES. However, current advanced AI-based decision aids are not ready to be incorporated into clinical practice for enabling diagnoses because they need to be validated and approved for application in clinics. What differentiates the AI system from the traditional medical devices is the fact that the former may change over time due to learning from new coming data. As for the regulation bodies, it becomes their task to design guideline for evaluation of AI’s performance and safety at each stage of its use. There is therefore no set recognized regulatory frames and benchmarks for defining what constitutes safe AI in healthcare (Topol, 2019). ETHICAL DILEMMAS. The application of artificial intelligence in diagnosis of patients leads to such critical issues as decision making, and liability as well as de-humanization of the health facilities. For instance, let’s say an AI diagnostic system identifies the disease wrongly; who is held liable-the creator of the healthcare organization or the AI itself? Furthermore, there is the potential risk that AI will take over the whole treatment process reducing patient-doctor relati...