Artificial Intelligence in Healthcare Assessment Answer

Artificial Intelligence in Healthcare

Rapid technical development has led to the creation of more patient-friendly procedures. A discussion regarding the effects of evolving technology has thus begun. Despite the fact that there are positive as well as negative results, people tend to favor the conclusion that benefits them the most. Advanced algorithms can be successfully solved because to the grouping of information utilized to produce answers (Jiang et al., 2017). It's important to stress that employing software for monitoring patients has enhanced treatment effectiveness by increasing caregiver awareness of patients who require extra care. The Socratic technique was developed to guarantee that teachers were promoting critical thinking before jumping to conclusions, and it is crucial for considering alternative approaches to an issue. The discussion will therefore focus on how the Socratic Method might be used to address the issue of artificial intelligence in the healthcare sector. In order to select the most suitable form of healthcare support, public health service providers acquire and evaluate data about the health of the community. They also do study to create fresh vaccinations and contain epidemics of disease. The objectives of public health initiatives are to regularly analyze public health concerns and to develop powerful community defense initiatives. Public health services like immunization, screening, and infection prevention are provided by health professionals (Jiang et al., 2017). They have helped fund life-saving initiatives and services, like rehabilitation, when they work with neighborhood clinics. They need the correct setting in order to completely realize their potential for providing high-quality services.

To assist people in overcoming obstacles, artificial intelligence (AI) has been used in a range of sectors. Technical advancements have resulted in the creation of treatments that have helped patients in their healing process because of the complexity of the medical industry. Due to the fact that people prefer to be treated by actual humans, professionals have only approved a small number of proposals despite substantial advancements in artificial intelligence in the area of healthcare. Hospitals may use big data along with the internet of things to manage patient information and pinpoint areas that need improvement. According to Yu, Beam, and Kohane (2018), physicians have been noted to diagnose their patients' ailments at an extremely slow rate. According to the research, artificial intelligence may be utilized to make sure that urgent actions are taken as soon as feasible to aid in patient care. Modern algorithmic technology has created a new set of ethical dilemmas. One of the ethical issues with this technology is the greater risk to patient security and privacy. Patients' confidential data is not secured, despite the fact that diagnosis is essential. Machine learning, coding, and algorithm frameworks can be used to hack into and share patient information (Jiang et al., 2017). The devices' functionality is controlled by computer systems, which can find flaws despite the fact that they were built by people. Machine learning is hence prone to errors, which might result in data manipulation. The machine can't adequately safeguard the patient's data as an outcome.

Some patients are also made to use unacceptable medical devices. Due to ethical issues and personal preferences, a great number of people favor physician therapy over machine therapy.

Advantages and Disadvantages of AI

Artificial intelligence will always have power and will always work, just like human-made technological advances and robots do. Precision and accuracy raised by artificial intelligence will be advantageous to many different industries, including the medical sector. All labor-intensive repetitive tasks will be completed by it, allowing for more precise and timely diagnostic and medical alternatives as well as an offset in the physical strain of medical professionals. Robots can be programmed to do a variety of tasks, including as continuously monitoring patients, offering advice, suggesting alternative treatments, and encouraging patients to take their medications, enabling doctors and other healthcare professionals to concentrate on other duties. Because AI is more intelligent than humans, there is a risk that it will be misused in the healthcare industry (Jiang et al., 2017). Artificial intelligence is used in the healthcare industry to help with anything from regular activities to data management and pharmaceutical development. Given the industry's continual expansion and commitment to excellence, it is not unexpected that hospitals and other medical facilities are implementing machine learning and artificial intelligence. By providing data-driven suggestions, machine learning and artificial intelligence can help physicians in the medical field. These tools may identify patterns and provide automated knowledge using data and algorithms, which is useful for maintaining medical records, planning treatments, and even conducting digital consultations. The following is a link to a post we wrote for a blog about the 10 most effective AI applications in healthcare (Kuiler et al., 2017). While there are many advantages to artificial intelligence in the medical sector, a lot of professionals differ on the advantages and disadvantages of using algorithms and data-driven technology to offer patient care. We've compiled a list of the current benefits and drawbacks of artificial intelligence in the medical sector to help you make an informed choice.

Advantages of Artificial Intelligence in Healthcare

Clinical research, healthcare information, and genetic data are just a few examples of the kind of data that artificial intelligence-enabled technology can evaluate far more rapidly than humans are able to, which can help doctors diagnose patients. AI can be utilized to automate clerical activities including inspection, input of data, and record management. Medical staff may devote more time to patient care if they spend a lesser amount of time on managerial tasks. Online health assessments and consultations - AI can help people manage their health while also providing important data to healthcare providers thanks to wearable health devices like the Apple Watch and FitBit as well as online consultations on mobile devices (Kuiler et al., 2017).

Disadvantages of Artificial Intelligence in Healthcare

Complicated training – For AI technology to function as intended, it needs to be carefully instructed using handpicked data sets. However, due to privacy considerations, it may be difficult to gain access to some of the data needed to give AI learning the comprehensiveness and breadth of knowledge it needs. Transformation can be difficult. This is true for all industries. The medical sector demands proof that AI will be helpful to patients and a plan for persuading financiers that the funding will be successful due to the crucial significance of the healthcare industry for patient care. Everyone who uses AI technology needs to be aware of how it operates and how it might help them with daily chores (Kuiler et al., 2017).

Future of Artificial Intelligence in Health Care

Artificial intelligence (AI) is transforming how we communicate with one another, take in information, and obtain goods and services across sectors. Artificial intelligence is already changing how patients are treated, how doctors practice, and how pharmaceutical companies operate in the healthcare sector. It's just the start of the journey. Artificial intelligence has three different types of applications in the field of health care and is present all over, from our mobile devices to our supply networks.

A Patient-centered Artificial Intelligence

Physicians were instrumental in the advancement of artificial intelligence. Organizational and operational goals are achieved using artificial intelligence (AI).

The future of artificial intelligence in the medical sector could include every detail from taking phone calls to examining medical information, population health trending and statistical analysis, creating therapeutic drugs and equipment, analyzing radiology images, generating clinical diagnoses and treatment schemes, and even interacting with patients (Kuiler et al., 2017).

The following is a list of where artificial intelligence in healthcare is headed: a look at the applications of machine learning, natural language processing, and artificial intelligence in healthcare; A look at how artificial intelligence will impact healthcare over the coming ten years as new technology change how doctors and patients are treated.

An examination of artificial intelligence in the medical field in ten years as emerging technology change medicine and healthcare.

With self-service, chatbots, diagnostic computer-aided design tools, and radiology processing for data, AI is already improving convenience and performance, reducing costs, and expanding access to therapy for more patients. These advancements are also enabling the discovery of candidate compounds for use in drug research.

Although machine learning and natural language processing are currently used in the healthcare industry, their ability to enhance outcomes will only become more relevant, increasing clinician and provider performance and treatment standards. It should be simpler for patients to access treatment and participate in decision-making. Improve the speed at which new pharmacological drugs are created. Analytics can be used to access large, previously unexplored quantities of uncoded clinical data in order to influence health care. The greater promise is found in the synergies that may be achieved when using AI tools across the patient background, from diagnosis to medication to continuous health maintenance. Each AI tool is extremely helpful on its own.

Implementing AI in Health Care: Lessons Learned

From collaborating with clients on uses of artificial intelligence in healthcare, we learned the following: Early adoption should be given more time and resources because even small projects need more time and work to analyze business scenarios and prove the idea's validity. Applying open-source technologies and restricting modification lowers both price and complexity. Create systems that can handle a wide range of transaction values and lengths, as well as longer transactions and more demand. It is advisable to include personnel with experience in both technology and healthcare because they will be more knowledgeable about the wants and needs of end users as well as technological solutions. Select the information that will be utilized to "train" any AI/ML model with care: There is little doubt that the model is neither over-trained nor under-trained, and that it appropriately represents production data. The predicted return on investment (ROI) should take into account the duration and time period due to the ongoing nature of model training.

The use of artificial intelligence in the medical field has gained ground recently and isn't going away any time soon. AI in healthcare holds enormous promise since machine learning can help with a variety of functions, from mobile tutoring to pharmaceutical study. On the other hand, due to worries about data integrity, privacy, or the regrettable presence of various company divisions that obstruct data flow, many healthcare managers are reluctant to implement AI. Artificial intelligence in healthcare refers to the use of sophisticated algorithms to automate the completion of preset tasks. Data entered by scientists, doctors, and researchers into systems may be examined, understood, and even used to suggest answers to complex medical issues by newly developed algorithms. A wide range of healthcare applications utilize AI.

Here are some of the most innovative AI applications in the medical sector that you should be conscious of; some will be covered in more detail in the article, while others have previously received stand-alone pieces on Healthcare Weekly.

To diagnose particular illnesses, artificial intelligence is used in medical diagnostics. Here is a link to a study that experts in the field wrote. A revolutionary artificial intelligence framework for cancer diagnosis and prognosis was developed in March 2019. creation of new medications In order to speed up the lengthy timeframes and processes involved in creating and getting pharmaceuticals to market, several health and pharmaceutical businesses are turning to artificial intelligence. Download our report, The Pharmaceutical Sector in the Age of Artificial Intelligence: A Global Perspective, if you're curious to learn more. A Promising Future. Clinical studies, on the contrary, have proven to be a complete failure. The majority of clinical trials are carried out on paper, without the use of integrated software to track progress, gather information, or evaluate the results of pharmaceutical triage. The effect of artificial intelligence on clinical trials is covered in this article (Carlson, 2019). Robert Chu, the CEO of Embleema, is also a guest on the Healthcare Weekly podcast, which you might find interesting. We talk about Embleema's use of blockchain and AI to transform clinical trials. Download our Global "Blockchain in Healthcare" Report: The 2019 Ultimate Guide for Every Executive if you want to learn more about the use of blockchain in healthcare.

Medicine's recent focus has been on pain treatment. We might be able to construct simulated environments that can divert people away from their current source of pain by combining virtual reality and artificial intelligence, which could help with the opioid issue. Here is more information regarding how this functions. Another wonderful example of how AI and VR may coexist together is the Johnson & Johnson Reality Program, which we previously discussed (Carlson, 2019). In conclusion, J&J created a virtual setting to educate doctors using rule-based algorithms in a controlled environment. The following patient outcomes have improved: AI-driven treatments and results have an opportunity to greatly improve the results for patients in a number of different ways. Start by reading our article on the ten ways Alexa is changing the healthcare industry and then listening to our Healthcare Every week podcast with Sangeeta Agarwal, CEO of Helpsy. The world's first artificial intelligence-powered nurse, created by Helps, is a chatbot that aids cancer patients receive therapy. This is just a small selection of healthcare applications for artificial intelligence. Consider a few additional real-world examples that every CEO in the healthcare industry should be aware of in 2019.

Applications of Artificial Intelligence in Healthcare

The audience was enamored with the protagonist's plump robotic healthcare assistant. We were unaware of the amazing technology at the time, which was centered on artificial intelligence and created to scan a patient's body for diseases or injuries while also evaluating the environment, administering treatment, and even attending to the patient's emotional requirements (Carlson, 2019). Despite the fact that Baymax seems to be a wholly made-up creature from a kid's movie, technology and robotics experts from all over the world are working to make healthcare AI a reality rather than a science fiction. The potential of technology, in particular artificial intelligence, has increased as a result in a variety of industries, including as learning, football, the production process, and healthcare. Another artificial intelligence-focused business, Insilico, has used a different approach, using AI to create medications that are not yet present in either chemical library. A technique for using AI to simulate clinical trials before they are conducted by humans has also been developed, demonstrating AI's great potential.

Summary and Conclusion

Massive data sets are analyzed and understood by artificial intelligence in the medical sector to help clinicians make better decisions, maintain patient information more efficiently, create customized treatment plans from intricate records, and find new medicines. Artificial intelligence in healthcare has the potential to help clinicians make better judgments more rapidly by identifying trends in medical issues much more precisely than the human brain. In an area where actions taken can have a profound impact on patients' lives, saving time and finding solutions to problems are essential. In the healthcare industry, AI is a great addition to physician and patient data administration (Carlson, 2019). By consulting doctors less frequently or never, patients who use telemedicine relieve the burden on healthcare professionals while also improving patient comfort. Doctors can complement their knowledge and skills on-the-job with AI-driven training modules, showcasing AI's ability to handle data in the healthcare industry. Real-time data collection in mobile tutoring platforms allows for the provision of patient advice and an improvement in treatment outcomes. Since businesses started using artificial intelligence to give quick diagnostics via mobile applications, telemedicine has grown in popularity.

The requirements of both large and small biotechnology enterprises in terms of innovation are still being met by acquisitions. In terms of firm ownership, there is a lot of control to relinquish as artificial intelligence develops. Older and larger businesses now have more options for acquiring information, systems, and even the people who are driving technical advancements as a result of innovators merging artificial intelligence and healthcare. Pharmaceutical companies may incorporate cutting-edge technology into a time-consuming and expensive process thanks to another key application of AI in this sector of the economy.

The advantages of AI in drug testing and diagnosis are instantly apparent, with a focus on reducing processing time and detection of patterns (Carlson, 2019). In the beginning phases of medication development, firms like Verge Genomics and Benevolent AI are well known for using algorithms to comb through vast amounts of information for sequences that are too complicated for humans to see. This saves time and allows us to innovate in ways we would not have been able to otherwise.

References

Carlson, E. R., & McGowan, E. (2019). A Foundational Framework for Andragogy in Oral and Maxillofacial Surgery I: General Considerations. Journal of Oral and Maxillofacial Surgery, 77(5), 891-893.

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: past, present, and future. Stroke and vascular neurology, 2(4), 230-243.

Kuiler, E. W., & McNeely, C. L. (2018). Federal big data analytics in the health domain: An ontological approach to data interoperability. In Federal Data Science (pp. 161-176). Academic Press.

Yu, K. H., Beam, A. L., & Kohane, I. S. (2018). Artificial intelligence in healthcare. Nature biomedical engineering, 2(10), 719 

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