Computer Science, Machine Learning, Healthcare

Introduction

The healthcare systems worldwide have been quite expensive, whereas great ineptitudes are often present within the systems. More so, a large proportion of population cannot accessed healthcare services adequately. However, several healthcare industries have embraced digital care attributed to advancement in technology. Nowadays, documentation, billing, and regulatory processes have been automated, thus enabling caregivers to meet most of the patient’s needs. Moreover, the shortage of physicians in various sectors in the medical field can only be compensated with the digital infrastructure that can be accessed globally and deliver data real-time. An advanced innovation has led to software becoming more intelligent. Application of artificial intelligence (AI) to the healthcare industry is likely to promote not only efficiency but also efficacy, thus enabling the systems to address the issues mentioned.

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AI/Machine Leaning Impact on Healthcare

One key reason for AI integration to the healthcare sector is the amount of data that has been growing exponentially since the industry started using digital systems like high-resolution radiology videos and images, EHRs, and digitized laboratory slides. Moreover, enormous portions of data have been streamed from wearable devices like implantable defibrillators, monitoring devices, and glucose monitoring machines. Therefore, AI allows the system to access, process, and evaluate vast amounts of information. Accordingly, one will witness improved overall healthcare outcomes, as improved financial reporting capabilities, organization decision making, and operative performance will take place (Gupta and Mohammad).

Another prospect for AI application in the medical industry is the development of new treatments. The process of developing new drugs involves testing of chemical compounds against any probable combination of various genetic mutations and other conditions tailoring medicine to specific ailment. Development of new drugs is time-consuming and costly. Gupta and Mohammad claim that an average period of twelve to fourteen years is taken to produce new treatment. In addition, approximately $2.6 billion is spent on discovery.

High cost and long period restrict the number of drugs to be discovered. Integration of AI/Machine Learning in drug discovery may aid machines to explore how researchers carried out their previous processes, hence being able to predict the type of experiment to carry out and define side effects of the drug. AI can cut on cost and save time by testing enormous chemical compounds simultaneously. However, AI integration in research may face some challenges like bias where financial inducements may support the development of particular treatment over others. Likewise, research outcome may be explicit to only some societies, temporary, and partial.

Diagnosis entails complex procedures where clinicians have to consider different factors to decide on the type of ailment and treatment. Clinicians are still facing challenges in diagnosis of diseases due to a lack of machines or software that can analyze information and provide significant discernments. This has led to late detection of some symptoms, particularly when handling high-risk diseases like Alzheimer, cancer, and diabetes. Incorrect diagnoses cause significant harm like including superfluous testing, long stays in hospitals, morbidity and mortality, and malpractice cost. Use of AI in disease diagnosis can hasten the detection of indistinct signs of diseases (Gupta and Mohammad). Early detection of symptoms allows clinicians to administer treatment timely, thus increasing the chances of survival and reducing costs linked to readmissions.

Conclusion

Lastly, AI technologies like companion robots, virtual avatars, companion robots, and robotic surgery can be used to complement or substitute human clinicians. This will aid in curbing the shortage of healthcare givers, which is a major challenge globally. For example, AICure is used to monitor patients in a clinical care setting to ensure that they take their education in right dosage and at the appropriate time. AICure then records, collects, and disseminates the information to caregivers. Consequently, it can promote health outcomes since people will adhere to medication.

 

Work Cited

Gupta, Megh, and Qasim Mohammad. “Advances in AI and ML Are Reshaping Healthcare.” TechCrunch, 2017, https://techcrunch.com/2017/03/16/advances-in-ai-and-ml-are-reshaping-healthcare/. Accessed 29 Jan. 2020.