Medical providers and administrators can use AI in healthcare settings for faster workflows and support clinical decision-making

As artificial intelligence (AI) technology becomes more capable and widely adopted, the role of AI in healthcare continues to expand. From automation to communications, AI can help doctors, nurses, and administrators do more for their patients with fewer resources. Over the past few years, AI has aided in drug development and interpreting medical images, to say nothing of its role in robotics. If you work in a medical office, there’s a good chance that AI can improve your workflows, too.

In this guide, we’ll cover the uses of medical AI in the exam room, the front desk, and the pharmacy. We’ll also discuss the ethics of healthcare AI and how the technology might evolve in the near future. Whether you’re just starting to experiment with AI tools or you’ve been using them for years, now is the time to consider the full range of possibilities for AI in healthcare.

What is AI in healthcare?

Broadly speaking, AI in healthcare refers to when medical practices use artificial intelligence technologies to operate more efficiently and improve patient outcomes. To fully understand the term, it helps to lay out a few basic definitions:

• Artificial intelligence (AI) is any computer technology that performs tasks that normally require human intelligence.
• Machine learning (ML) is the process by which AI systems gather new information and refine their outputs over time.
• Generative AI is a tool that creates new text, images, audio, or video in response to a user’s input, usually by communicating with them through LLMs.
• Large language models (LLMs) are a type of generative AI trained on massive amounts of text. Tools built on LLMs, such as ChatGPT or Google Gemini, accept plain-language prompts and use them to respond.

All of these technologies are currently in use in the healthcare sphere, and all of them rely on having a rich library of high-quality data. That data often comes from digitized patient records. Digitization requires an efficient document scanner and powerful scanning software, so you’ll want to invest in those if implementing AI systems is your eventual goal.

What can AI do in healthcare settings?

How is AI used in healthcare? This technology has far-reaching implications for the healthcare sector, from accelerated admin work to better cybersecurity:

• Notetaking: When providers split their attention between treating patients and taking notes, they can leave the patient feeling ignored. AI can help by automatically transcribing conversations between patients and providers. That leads to higher-quality, more complete notes without affecting the clinician’s bedside manner. It also makes reviewing those notes faster and easier.
• Administrative work: Treating patients requires a lot of clerical work. Every patient must be processed. The key points of their visit must be recorded and added to their electronic health records (EHRs). Those records must be sorted for easy access. AI can help by recognizing different types of documents and automatically sorting and tagging them. It can also examine forms for key information and populate it to digital records. For example, PaperStream AI can automatically recognize many of the different document types that are processed in a healthcare office, helping administrators and front desk receptionists alike reduce repetitive data entry.
• Care availability: Patients often have questions they want answered outside of standard office hours. AI chatbots available on a practice’s site can answer simple patient questions at any time of day. These bots recognize common questions and provide answers that a clinician has pre-approved. More complex questions could trigger a notification for the clinician to respond when they’re back in the office.
• Fraud protection: According to the FBI, medical fraud causes tens of billions of dollars in losses each year. AI systems can recognize unusual patterns in medical payments. If a provider bills for unnecessary procedures, or ones they didn’t perform, AI could help spot those irregularities. This technology can also alert patients if someone files a false claim in their name.

AI in healthcare examples

AI in healthcare examples are easy to find. That's because the medical field has employed AI technology since 1971. The first healthcare AI was called INTERNIST-1, and it used computer algorithms to deduce probable diagnoses for patients. While medical AI used to be restricted to one program at a single university, it’s now available everywhere in the healthcare field:

• Assisted diagnosis: AIs such as INTERNIST-1 would simply weigh various vital signs and risk factors to come up with the most likely diagnosis. In the modern era, medical AIs can make much more personalized predictions. Thanks to cloud computing, AIs can learn from enormous data sets, including medical images. At present, medical AIs can identify certain types of liver and kidney disease with up to 99% accuracy.
• Treatment plan creation: In the past, clinicians would have to come up with treatment plans based on relatively broad criteria, such as a patient's age, sex, or weight. This meant having to make frequent, minor adjustments if the initial treatment proved ineffective. AI algorithms can create a treatment plan much faster than a human doctor can, and with potentially better results.
• Medical robots: Medical robots are beginning to integrate adaptive AI technology. Companion robots for elderly or infirm individuals can interact with humans and "learn" behaviors over time. Smart prostheses can adapt to an individual's biology and movement patterns. Expect this field to grow considerably as the role of AI in healthcare evolves.
• Drug discovery and development: AI algorithms can help create, refine, and administer pharmaceuticals. Using genomic data, AIs can predict how a drug might interact with the human body, down to the level of individual proteins. AI algorithms can also determine how well a given drug performed in a clinical trial, and even whether an existing drug might have secondary uses.
• Equipment manufacturing processes: The Food and Drug Administration has approved hundreds of AI-enabled medical devices for sale in the United States. These tools represent just about every field in healthcare, from clinical chemistry to gastroenterology and microbiology. The FDA expects the list of AI-enabled medical devices to keep growing in the future, particularly in radiology.

Ethics of AI in healthcare

For each of these examples of AI in healthcare, it's important to remember that AI is not an autonomous entity. Artificial intelligence requires human oversight, particularly in a field as sensitive as medicine.

First and foremost are the issues of transparency and privacy. Organizations should evaluate applicable privacy, consent, and data-governance requirements before using patient information in AI or machine-learning systems and should follow all applicable laws, regulations, and internal policies.. Similarly, if a patient might require a medical device that collects biometric data, you should tell them exactly what data the device collects, and how you intend to use it, following appropriate processes, applicable regulatory requirements and policies established in your organization . Remember that electronic patient records are covered under the Health Insurance Portability and Accountability Act (HIPAA), so any AI systems you implement will have to adhere to HIPAA standards and other applicable laws and regulations.

Although AI has been around in the medical field for a long time, there is no universal ethical standard for using it. As such, you should defer to local laws, your organization's code of ethics, and your own best moral judgment. The UNESCO "Recommendation on the Ethics of Artificial Intelligence" may also be useful.

In addition, AI systems should be used with appropriate human oversight and should not replace professional medical judgment. Organizations should evaluate AI tools for accuracy, bias, privacy, security, transparency, and regulatory compliance before deployment, particularly when tools may influence patient care decisions.

What is the economic impact of AI in healthcare?

We are already seeing the economic impact of AI in healthcare in improved operational efficiency and reduced costs. AI helps drive healthcare providers' financial success by eliminating repetitive manual tasks, which reduces staffing costs and frees skilled employees to focus on high-value work.

Morgan Stanley estimates that AI in healthcare could save between $400 billion and $1.5 trillion over the next few decades. These savings represent everything from better use of hospital resources to faster, more effective drug development.

On the other hand, AI adoption could increase administrative costs in the near future. The exact nature of short-term costs versus long-term benefits of AI in the healthcare industry may not become clear for at least a few more years.

How can AI improve patient outcomes?

Through decision support, data analysis, and imaging assistance, AI can assist with a wide range of challenges the healthcare industry faces. AI can help patients achieve better outcomes through:

• Prescription auditing: Since patients often need to take prescription drugs, the potential for incorrect dosage, negative drug-drug interactions, and pharmaceutical misuse is always present. Machine learning can study how various drugs interact with each other, and help draw connections that might not be obvious to a human observer.
• Patient data analytics: While each patient is unique, each patient is also part of a larger community. By using patient data analytics, AI algorithms can draw conclusions based on whole populations. Whether linked by region, demographics, or a specific medical condition, analyzing large groups of people can lead to novel discoveries.
• Medical imaging analysis: AI-assisted imaging tools can help identify patterns and abnormalities that may be difficult to detect through manual review alone. As such, AI is an invaluable tool in radiology, where it can help doctors detect cancerous growths more efficiently.
• Genome sequencing: Genome sequencing is a field that requires both sophisticated AI algorithms and raw computing power. Genomics research generates huge quantities of difficult-to-parse data. AI programs can analyze that information and use it to discover specific genetic sequences that cause diseases.

Benefits of AI in healthcare

The benefits of AI in healthcare apply to just about every part of the industry. Whether you work in managed care, facilities, pharmaceuticals, or medical equipment, integrating AI into your operations can streamline your workload and help improve patient outcomes.

Benefits of AI in managed care

Assisted diagnosis is one of AI's most important applications in the healthcare field. Today, medical AIs can cross-reference vital signs, lab tests, medical images, and patient history to assist healthcare providers in identifying and diagnosing diseases more quickly and in some cases, more accurately. Similar AI programs can also recommend full treatment courses. Unlike human clinicians, AIs can work through this data for dozens of patients at a time.

Benefits of AI in healthcare facilities

Many companies in the medical industry currently use AI chatbots, including healthcare facilities. Chatbots can answer questions, collect data, and even schedule appointments for patients. That means less time on the phone for front-office staff, and less time manually sorting through paperwork. Regardless of how refined your AI options become, though, you should still make it easy for patients (and insurance reps) to talk to a real person. You should also test your chatbot now and then to make sure it's not hallucinating.

Benefits of AI in pharmaceuticals

One intriguing study from the scientific journal Artificial Intelligence Review proposed 11 different ways for AI to enhance pharmaceutical research. These include everything from modeling protein folding, to analyzing clinical trials, to finding secondary uses for existing drugs. However, health records, such as patient information gathered during clinical trials, are vulnerable to data breaches, making cybersecurity doubly important.

The future of AI in healthcare

As the power and capabilities of AI continue to evolve, the potential roles AI could play in healthcare are expanding. The future of AI in healthcare covers a wide range of applications, from enhancing diagnostic capabilities to providing new opportunities for education.

The World Health Organization (WHO) described five areas where it sees AI being of particular use in healthcare:

1. Diagnosis and clinical care: AI can analyze images to diagnose tumors and other health issues. Newer AI models, such as COMPOSER, can also predict a patient’s risks for certain illnesses by studying demographics, medical history, vital signs, medications, and lab results.
2. Patient-guided use: Patient portals give patients online access to their medical records and results. With AI enhancements, these portals could estimate costs of treatments, recommend potential providers, or provide personalized chatbots to discuss symptoms.
3. Administrative and clerical tasks: AI can automate many standard administrative tasks, such as sending appointment reminder emails and paying recurring invoices. Now, it’s beginning to tackle healthcare-specific jobs, such as optimizing operating room (OR) availability.
4. Medical and nursing education: As students study to become doctors and nurses, they accumulate massive amounts of digital training materials. Specialized LLMs can help them sift through this data quickly, even if they don’t know exactly what they’re looking for.
5. Scientific research and drug development: AI can process repetitive information and pick up subtle patterns much more effectively than humans can. The Human Cell Atlas, for example, wants to discover how every cell in the human body interacts, and has used AI to learn more about rare cancers.

Our recommendation: RICOH fi-8170

We recommend the RICOH fi-8170 to digitize healthcare records for use in AI workflows. This versatile device can scan up to 70 double-sided pages per minute, and comes with a 100-sheet automatic document feeder (ADF) to keep paperwork flowing smoothly. At 11.8 x 6.7 x 6.4 inches and 8.8 pounds, this scanner fits easily in most office setups. Our full lineup of RICOH fi Series scanners has options to suit almost any healthcare office setup, especially when paired with the right software.

PaperStream Capture can automatically tag files with metadata and route them to specific folders. You can also turn scanned documents into fully editable digital text files.

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