The role of Artificial Intelligence in healthcare
This essay discusses Artificial Intelligence as a transformative force in health care through current applications, benefits, and challenges that raise ethical concerns. The usefulness of AI in diagnostics, personalized treatment, predictive analytics, and administrative support will be outlined, with a bright potential for further increasing speed, accuracy, and cost-efficiency in healthcare delivery. Next, the discussion addresses challenges relating to data quality, algorithmic bias, and the need for ethical safeguards regarding privacy and accountability. The essay concludes by stating the way forward and how many experts come together with the view of implementing AI responsibly. This essay can, therefore, serve as an informative resource on the potential and limitations of AI in modern healthcare.
November 10, 2024
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The Role of Artificial Intelligence in Healthcare
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The Role of Artificial Intelligence in Healthcare
Artificial Intelligence (AI) has emerged as a transformative force in healthcare.
Diagnostics, treatment, patient management, and administrative tasks of patients have
changed. Machine learning, natural language processing, and other AI technologies have
impressively enhanced timeliness, personalization, and efficiency within the healthcare
system to care for patients. This essay delineates the role of AI in health by describing
applications, benefits, challenges, and ethical implications of AI use in healthcare. The
functioning of AI in the betterment of health outcomes and the complexities in healthcare will
be more easily understood by observing the current uses of AI as well as its predicted future
impact.
Application of AI in Healthcare
Recently, with the increase in medical imaging, predictive analytics, and
administrative tasks, the applications of AI have grown significantly in healthcare. Machine
learning algorithms analyze images of medical conditions like tumors and fractures. Some AI
tools applied in dermatology are able to attain diagnostic accuracy comparable to that of
dermatologists. This furthered diagnosis at an early stage of the disease in cases when
specialists are unavailable (Esteva et al., 2017). Predictive analytics also forms one of the
major critical applications of AI in assessing patient data models for the risks of diseases and
initiating early prevention against heart disease or diabetes that ultimately helps improve
outcomes. Wang et al. (2019) further note that AI enhances administrative efficiency through
appointment scheduling, EHR management, and medical transcription. NLP algorithms
facilitate data organization by reducing the element of human error, which will, over time,
reduce healthcare costs and enhance the time professionals have to devote directly to patient
care.
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Advantages of AI in Healthcare
AI improves health care significantly with increased speed, accuracy, and
cost-effectiveness. Large volumes of data are processed by algorithms in far less time than
any human brain, identifying patterns of diseases-cancer, and genetic disorders for which
several data sources need to be merged. Powerful data analysis is thus very important for an
on-time diagnosis. AI also covers individualized medicine-personalized therapy for particular
patients according to their genetic background, lifestyle, and case history (Topol, 2019). For
instance, IBM Watson Health uses patient data to suggest specific cancer treatments. This
will raise the success rate of treatments and lower side effects. Similarly, automation by AI in
administrative tasks, backed by optimized resource use, slashes costs (Topol, 2019).
According to a report from McKinsey, AI could save the US healthcare system as much as
$150 billion annually by 2026, and it could equally benefit providers and patients by bending
the curve of overall medical costs (Jiang et al., 2017).
Challenges in Implementing AI in Healthcare
Despite its potential, AI in healthcare faces several challenges. These include the issue
of high-quality and standardized data. AI algorithms need large quantities of diverse,
high-quality datasets to do their job right. Primarily, they come fragmented across several
systems, and pooling comprehensive datasets for training AI is difficult (Reddy et al., 2020).
Regulating data privacy, such as through HIPAA, complicates this ability to share or access
such data.
Then, there's the potential for algorithmic bias. Thus, AI that has been trained on
biased data can sometimes arrive at results that are simply wrong, and that hurt patient care.
For instance, when only one demographic group provides the bulk of training to an AI
diagnostic tool, then that tool is not good for patients who are not of that group (Reddy et al.,
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2020). These biases may only be surmounted through consideration of the choice of training
data and by ongoing evaluation of the performance of AI systems with respect to equitable
healthcare outcomes.
Another fear in this regard is that AI will replace the roles of health professionals.
Much as AI can support diagnostics and decision-making, a human touch is also used in
caring. Many patients require that aspect of assurance, comfort, and personal experience that
no form of AI can offer them (Reddy et al., 2020). Though AI will alter the nature of tasks
undertaken by healthcare providers, human experience and emotional intelligence are
important aspects in patient care.
Ethical Issues Associated with AI in Health
Ethics also arise in healthcare when considering patient privacy, informed consent,
and job loss. This is because AI is increasingly applied in managing or analyzing data related
to patients, so its privacy should be protected. Healthcare organizations must make sure these
AI systems comply with set regulations for the protection of patient information (Topol,
2019). This, in turn, implies granting informed consent- a patient's right to know whether and
when AI interferes in his/her care and what the data will be used for.
Other issues revolve around accountability in the decisions made due to AI-powered
healthcare. If the AI system misdiagnoses or suggests wrong treatment for a patient, it is
going to raise questions of accountability (Topol, 2019). In this case, it will be tough to tell
who is responsible the health provider, the AI developer, or the organization that has put the
technology to work (Topol, 2019). Certainly, we need to institute certain guidelines on issues
of accountability and liability that would help assuage these ethical concerns.
AI involvement in health also fosters apprehensions in the loss of jobs. Although AI
may accelerate such processes, many fear that there will be some compromise in certain
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healthcare job positions, specifically in terms of administrative-related work (Topol, 2019).
Experts, however, clear this up by explaining that AI does not actually take away human jobs
but rather complements them, since healthcare professionals can now pay more attention to
the more intricate and patient-centered services (Topol, 2019). It has been stated that in
dealing with such ethical issues regarding AI in healthcare, one must pay great attention to at
least patient rights and responsibility and possible impacts on the workforce.
Future Directions and Possible Impact
Artificial Intelligence in the future has further potential to add to healthcare.
Biomarkers will be able to monitor blood tests, genetic data, and even wearable devices
because advanced models in machine learning will be developed to help the condition of
diseases to be found earlier (Topol, 2019). Other possible future uses of AI could include
such advanced surgery as robotic surgeries or possibly AI-driven therapy to treat mental
health with conversational agents. With the growing capacity, AI technology is bound to be
involved in the health sector and bring about revolutionary changes in the mode of delivery
and receiving your and my health care (Topol, 2019).
As more institutions realize the potential of AI in improving outcomes and cutting
costs, investment in its research and development will continue to increase (Topol, 2019). All
that will take some successful integration of AI in health for the current challenges to be
overcome and ethical guidelines defined-a process that will need policymakers, healthcare
providers, and technology developers to work together (Topol, 2019). Above all, it will be
paramount to ensure that AI technologies are available to a wide variety of populations and
deployed responsibly to secure their full benefit.
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Conclusion
This essay has demonstrated that Artificial AI might bring a sea change to healthcare
by offering better diagnostic precision, more personalized care, and economies of cost in
many ways. However, while implementing AI in healthcare, a number of challenges are faced
with respect to data quality, algorithmic bias, and other ethical issues concerning patient
privacy and accountability. This will require overcoming such challenges and laying down
standards on ethics in the use of AI if healthcare systems have to fully exploit the capabilities
of AI. With each evolution of AI, its application in health continuously improves in ways that
portend better patient outcomes with a more effective healthcare system for both providers
and patients.
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References
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017).
Dermatologist-level classification of skin cancer with deep neural networks. Nature,
542(7639), 115-118.
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.
Reddy, S., Fox, J., & Purohit, M. P. (2020). Artificial intelligence-enabled healthcare
delivery. Journal of the Royal Society of Medicine,113(1), 4-10.
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial
intelligence. Nature Medicine,25(1), 44-56.
November 10, 2024
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Academic level:
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Type of paper:
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Discipline:
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* The sample essays are for browsing purposes only and are not to be submitted as original work to avoid issues with plagiarism.
