AI In Healthcare: Ways Doctors Can Leverage Artificial Intelligence

Artificial intelligence, also known as AI, has been making strides in almost every industry in the past decade — with the healthcare industry being no exception. It offers a great opportunity to improve efficiency and provide novel solutions that help you accomplish high-quality care outcomes. AI in healthcare is booming and is undoubtedly shaping the future of the industry.

The healthcare landscape has always been fluid, ever-changing and constantly growing, and has been required to adapt to the many outside factors exerting force. While AI certainly has pros and cons, it’s also taken an irreplaceable role in today’s healthcare system.

With challenges only growing, staffing shortages shaking healthcare facilities, and patient care outcomes at risk, the need for novel technologies offered by AI algorithms have never been more needed. 

But how do physicians ethically harvest innovative solutions offered by AI? Where can they be applied and what does this mean for the industry as a whole?

AI in healthcare is unequivocally here to stay but leveraging its full potential is a work in progress. To understand this better, let’s dive into some examples of how AI is applied in healthcare today.

How AI in Healthcare Can Be Leveraged for More Efficiency

There are a multitude of ways AI tools can be applied to interactions with patients, operations within the clinical setting, and the process of making reliable predictions of future disease outcomes based on patient data. For AI in healthcare, the limit is the sky and so is its potential.

Artificial intelligence is the ability of computers or robots to conduct assigned tasks that have been previously reserved for “intelligent” beings, i.e. humans.¹ Designed to make our lives easier and help us face the growing industry demands, the reach of AI technologies is expanding.

Below we compiled a list of the most profound AI technologies in healthcare.

AI for Medical Diagnosis & Preventative Care

One of the most impressive AI technologies aids in the diagnosis of medical conditions and disease prediction. Using an array of AI techniques, as well as machine and deep learning models, AI can be used for medical diagnosis.

As AI heavily relies on patient and disease data fed into its algorithms, the verification of the accuracy of all clinical data is critical. Ongoing data intake, examinations, and potential changes in the patient’s symptoms should be clearly reflected.

AI has the ability to compile clinical data, assess symptoms, and analyze diagnostic tests to draw conclusions about a patient’s health status, significantly helping with treatment and preventative care. In fact, AI for medical diagnosis has shown tremendous success already. 

Researchers found that 47% of cases spanning 35 diagnoses, had providers select an AI-suggested diagnosis with ≥95% accuracy.² In 69% of the cases, providers selected a medical diagnosis recommended by AI with a ≥90% likelihood.²

While caution is advised and human surveillance is necessitated, researchers now better understand how to train AI for medical diagnosis.³ This means that AI solution proposals can be more easily tailored to the circumstances surrounding patients, using more specific and accurate sets of data.³ 

AI in Medical Imaging

Medical imaging is another area where artificial intelligence can be applied, supporting the assessment of disease development while simultaneously aiding in the making of more precise clinical analyses for patients. AI is increasingly used in the medical imaging field and has the potential to provide more accurate disease screening. 

Medical images have grown of crucial importance for physicians to diagnose and monitor conditions that depend on the visualization of internal organs and bones. It’s used for a wide range of issues, ranging from cardiac events, kidney stones, and brain scans, to X-rays for fractures and more.

AI has enabled clinicians to recognize disease patterns, offer quantitative evaluations of radiographic properties, and allow for the processing of high numbers of images.⁴ Thus, AI in medical imaging is emerging as a valuable asset able to identify disease characteristics with remarkable efficiency.

Adding Efficiency to the Healthcare Industry

One of the most profound accomplishments of AI in healthcare is its ability to increase productivity, automate everyday activities, and boost organizational efficiency. Reduced labor costs and lowered expenses can be particularly beneficial to organizations striving to increase revenue and combat staffing shortages.

Why that’s important?

The average nurse spends about 25% of her time on administrative tasks, such as clinical documentation and charting.⁵ This means that a quarter of her time is spent on repetitive tasks that prevent active patient engagement. 

Artificial intelligence can provide time-saving tools that reduce the administrative burden on healthcare staff, take on scheduling and billing tasks, support disease screening, and help submit prescriptions — all whilst eliminating human error and delays. 

As algorithms become smarter and AI is trained in a more targeted fashion, it can take on many more activities previously performed by humans. AI tools continue to improve and expand, making increased efficiency one of the most important byproducts of this exceptional technology.

AI Pros and Cons

Just like most novel technologies, AI has both pros and cons. And while the benefits of AI outweigh the risks and concerns, they nonetheless exist. Changes in healthcare and its corresponding technological tools and applications are, however, necessary to push medicine forward.

Artificial intelligence enhances the jobs of the many vastly overworked healthcare professionals by increasing efficiency and automating time-consuming administrative tasks. In addition, AI significantly helps eliminate human error and oversight, which is critical in proper disease identification and treatment.

While supporting growing staffing shortages, facilitating research, and promoting new drug development, AI also increases patient engagement and can provide better and more consistent clinical outcomes.

However, since there are pros and cons to AI, it is advised to use AI technologies with caution and understand the type of algorithms and patient data used by AI models. Data generated by AI can be one-sided, produce biased results, and represent unconscious preferences or tendencies that went into the data collection process.

A further potential concern is unemployment as AI is beginning to replace human tasks and jobs. In addition, patient privacy and security risks can further affect AI-related processes which require clinicians to implement effective safeguards while using AI technologies.

Novel Technologies at Your Fingertips with Vivlio Health

Innovative tools can greatly enhance the lives of patients and clinical staff, including the use of AI in healthcare — supporting productivity and efficiency, producing better health outcomes, and paving the path for more effective operations within clinical organizations.

Vivlio Health supports innovation and efficiency and was founded by healthcare professionals who strive to improve processes within the healthcare industry. We offer a solution that can save your medical staff valuable time while offering the opportunity to focus on more pressing clinical tasks needed to improve patient outcomes.

With efficiency in mind, Vivlio Health developed a cloud-based tool to access your patient’s medical records, without gaps, and precisely when you need it. No more chasing other physicians and working through incomplete patient data! Find the clinical information that you need for your evaluation without delays and extract what’s relevant for you.

Ready to take the reins and lead your practice on the path of innovation and efficiency? Reach out to us for a free demo and we’ll be happy to answer any questions you may have. Let’s do this!

 

References:
  1. Tang X. (2019). The role of artificial intelligence in medical imaging research. BJR open, 2(1), 20190031. https://doi.org/10.1259/bjro.20190031.
  2. Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of ambient intelligence and humanized computing, 14(7), 8459–8486. https://doi.org/10.1007/s12652-021-03612-z.
  3. Dan Zeltzer, Lee Herzog, Yishai Pickman, Yael Steuerman, Ran Ilan Ber, Zehavi Kugler, Ran Shaul, Jon O. Ebbert, Diagnostic Accuracy of Artificial Intelligence in Virtual Primary Care, Mayo Clinic Proceedings: Digital Health, Volume 1, Issue 4, 2023, Pages 480-489, ISSN 2949-7612, https://doi.org/10.1016/j.mcpdig.2023.08.002.
  4. Copeland, BJ. “Artificial intelligence (AI) | Definition, Examples, Types, Applications, Companies, & Facts.” Britannica, https://www.britannica.com/technology/artificial-intelligence. Accessed 3 January 2024.