Introduction

Why is it important for nurses to be aware of emerging and current technological trends in health care? Nurses should be informed regarding potential and newly developed technology as they or their patients may be introduced to these technologies in the course of their health care, whether in the form of treatment or participation in a clinical trial.

• Proactive knowledge of current trends enhances clinical judgment in the application of nursing process toward positive outcomes
• By reflecting awareness, the nurse can instill trust and confidence in patients
• Knowledge of current trends can assist the nurse in teaching patients what to expect
• Knowledge of current trends can also assist the nurse in answering questions from patients and family members

Trending Topics

Technology is advancing at rapid speeds across all industries, including health care. Many of the same technologies currently trending in health-care sectors have applications in other industries tailored for their specific needs (e.g., finance, government, manufacturing, real estate, retail). We are overwhelmed to some degree with all the new technological developments in our world and workplaces. Many of the applications (apps) available for use in general are both helpful and efficient; others are dreadful. Health and fitness apps are a popular example of what is considered helpful. Some of the top health and fitness apps use artificial intelligence to meet rosy expectations with all the smartphone bells and whistles. This is where differences become apparent between choices the general consumer can make compared to choices that may be available to health-care consumers. A simple analogy often used to demonstrate these differences is to compare over-the-counter products like nutritional supplements to prescription medications.

Examples of approved and developing applications of technology specific to health care are presented in this appendix. The HCP should decide which characteristics are most important when selecting technology for use in the provision of patient care. Some factors include whether they are regulated, provide evidence-based and clinically validated findings, employ high-quality software, require a prescription, are reimbursed by insurance, are competitively priced, and can meet cybersecurity requirements for the protection of patients’ private health information as described in HIPAA.

Health-related technologies are being successfully used to

• Teach/train health-care-related personnel
• Improve surgical outcomes
• Link outcome-driven education with patients at the point of care
• Manufacture prosthetic devices of superior quality and function
• Reteach cognitive (speech, attention, memory) and motor skills to patients in situations that may require extended recovery periods (e.g., concussion, spinal cord injury, stroke, or postamputation phantom limb pain)
• Provide pain management
• Provide music therapy to improve motor, speech, and other cognitive dysfunctions associated with neurological diseases
• Treat many diseases; the most significant effectiveness has been demonstrated in conditions with both behavioral and psychological components, such as Alzheimer disease, anxiety, asthma, attention deficit-hyperactivity disorder (ADHD), autism, cardiovascular diseases, chronic obstructive pulmonary disease, dementia, depression, diabetes (type 2), gastrointestinal problems, heart failure, hypertension, insomnia, obesity, substance use disorders, and tobacco addiction (smoking). The goal is to improve self-management skills and therefore improve outcomes.

Health care is moving away from dependence on a model that requires in-person contact for assessment, diagnosis, and treatment. New versions of post-Covid pandemic health care appeared when in-person contact was impossible; they may still include stand-alone in-person contact, offer other options, or use combinations including

• Reality Technologies
• Wearable, Implantable, and Ingestible Technologies
• Digital Therapeutics
• Additive Manufacturing: Three-Dimensional Printing
• Artificial Intelligence (AI)

Reality Technologies

What Are Virtual Reality (VR) and Augmented Reality (AR)? Remember the stereoscope, the View-Master, and the first PlayStation? VR as we know it evolved around the 1980s to 1990s from these inventions and continues to offer exciting possibilities in health care for today and beyond.

VR is technology that allows users to have a virtual (fictitious) experience using computer programs visualized through headsets; users believe they have been transported into a real world.

AR blends real-world and virtual elements by overlaying virtual digital objects into a real-world environment; objects are present and users interact with them as though they are part of the real world.

What is the difference between VR and AR?

What are some similarities between VR and AR?

1. There are three basic component types:
   • Hardware: equipment through which the three-dimensional images are projected, such as head-mounted devices (HMD), smart glasses or goggles, smartphones and computers
   • Software: programs with the technology to create VR and AR experiences
   • Applications or apps: the specific program the user experiences
2. Both technologies have low latency, which creates a more natural experience (minimal delay to real-time changes, such as movement)
3. Output devices allow the user to interact with virtual objects (e.g., gloves, headphones, joysticks, wands); multisensory sensations may be experienced (hearing, taste, touch, sight, smell), which also may give a user the perception of physical movement

But WAIT! There are two other types of reality technology! Mixed Reality (MR) and Extended Reality (XR) are combinations of AR and VR and can achieve many of the emerging successes of AR and VR but in an enhanced form. If you understand the basics of AR and VR, you can also grasp the MR and XR technologies, but it is not important to focus on identifying which form is which for the purpose of enjoying the content in this appendix. Spoiler Alert: XR can mean AR, VR, MR, or a combination of all three—but not the other way around. VR places our reality in an entirely simulated or artificial world; AR places a digital overlay on what we can actually see and interact with in the real world. MR combines VR and AR, merging the real-world experience with digital objects into an interactive reality. XR is a relatively new technology. It is what happens when the virtual world becomes indistinguishable from the real world. XR creates a more personalized, multisensory, immersive experience.

• Important trends for medical students, residents, fellows, physicians, nurses, and patients

  Teaching, Learning, and Implementation

  1. Learning environments changed during the pandemic when live classroom instruction came to a halt. Studies have shown that retention levels in a VR environment are 80% higher than retention levels obtained from lecture-based or textbook-focused curricula. The total immersion experienced in a VR environment removes all external distractions (e.g., cell phones, chatty students seated nearby, traffic sounds).
  2. Training and education presentations for health-care personnel have in some places moved from lecture, PowerPoint themes, and simulation mannequins to AR/VR-guided simulation experiences such as in advanced cardiovascular life support, basic life support, and specialty resuscitation; fire prevention in the operating room; role-playing with virtual patients; and numerous other clinical patient care situations.
  3. Medical education and the study of anatomy have changed—from using pictures or written descriptions in books to early-generation smartphone anatomy apps. The next step has taken place in a collaborative project between Case Western Reserve University and the Cleveland Clinic (partnered with Microsoft), to utilize an app called HoloAnatomy. The MR technology and app create a dynamic holographic model of the human body that features multiple positioning options, size scaling/magnification to provide fine detail, ability to incorporate instructor notes, attach labels to body parts, and use other special tools (e.g., pointers, highlighting, etc.). Multiple users are able to simultaneously access the app.
  4. Traditionally, medical students have learned how to perform surgery by either observing an actual surgery or by operating on a corpse. MR technology provides a third option where students can operate on cadavers in a MR environment that replicates performing surgery on a live patient. The simulation that takes place in a real-world environment responds to the student’s actions as if the procedure were real.
  5. VR and AR can help surgeons develop a detailed surgical plan and proactive interventions based on their ability to preoperatively evaluate three-dimensional images of the patient’s entire body, visualize the intended surgical site (e.g., knee, heart, hip), and reveal potential complications. In Seattle, Washington, an AR technology company (Proprio) is using imaging in combination with an AI platform to create and analyze three-dimensional preoperative images that can be stored and used during surgery. Other similar innovations:
     1. An AR/VR technology called ImmersiveTouch is used by Johns Hopkins, the University of Chicago, and the University of Texas to collaborate on surgical strategies as well as to educate and train. Osso VR is another simulation-based virtual platform designed so that multiple surgeons can practice complex orthopedic and spine procedures together using virtual tools.
     2. New York University, the Mayo Clinic, and the University of California, Los Angeles, use a VR product called Surgical Theater for preoperative planning and also as a personalized three-dimensional model to aid in preoperative education for the patient, explaining their medical issue and the surgical plan to correct it.
     3. A similar AR/VR product called Proximie can be used to scan, assess, and evaluate a patient’s body, identify health issues, develop a surgical plan, and educate the patient regarding their course of treatment.
  6. Haptic devices are AR-enabled technology extenders that provide tactile stimulation and are used to diagnose, train, and provide treatment. The haptic device creates tactile feedback when the user interacts with a virtual tool (training on new equipment) or with the actual physical object. Haptics are classified into three general groups: those that can be grasped (e.g., joystick), are wearable (e.g., hand exoskeletons or the magnetic resonance imaging [MRI] glove), or are touchable (virtual tool). Some examples include
     1. Robotic surgery
     2. Haptic gloves designed to present newer versions of surgical tools such as saws and drills
     3. The MRI glove is another type of haptic device that was developed to work as an extended component of functional MRI (fMRI) studies. The glove is made of materials that are pliable and allow a large degree of freedom of movement as well as being constructed from nonmetallic materials that do not interfere with MRI imaging. Images of concerted hand movements of bones, tendons, and ligaments can be captured and used to provide a more detailed understanding of hand anatomy, improve diagnosis and surgical correction of hand injuries, and develop better prosthetics.
  7. MR glasses that can display images and clinical data (e.g., heart rate, blood pressure) on top of a patient’s body. The glasses can be worn during situations that might include patient assessments, office procedures, or surgery.
  8. An AR/VR technology called TrueVision that is designed to convert conventional surgical microscopes into digital surgery systems for use by neurosurgeons, surgical ophthalmologists, and in miscrosurgeries (e.g., vascularized bone/breast flap transfer, complex wound reconstruction, digit replantation/transplantation).
  9. AR technology can assist patients in understanding how medications work in their body, using three-dimensional models.
  10. Experiments are being conducted in the United States and Europe to see if VR can be used for better health communication (e.g., to boost vaccination rates for Covid and seasonal flu). In the experiments conducted in the United States and Europe, the subject watches a scenario wearing 3D smart glasses. The 2022 U.S. simulation involved an adult trying to navigate through a crowd of infected and uninfected people. Participants who were hesitant to get the vaccine before the simulation reported an increase in their intention to likely receive the vaccine after their VR experience. The first-person experience also increased their sense of collective responsibility.

  Types of Treatment Using Reality Technology

  1. AccuVein is an AR tool used to visualize patent veins when starting IVs or performing blood draws. A handheld scanner is pointed at the arm, hand, or other site, and images of the vessels are projected on top of the skin; veins can be detected up to a depth of 10 mm.
  2. VR is being used successfully in pain management (physical and psychological; acute and chronic) and instigation of relaxation. Patients using this technology have reported less pain and a better overall health-care experience. Examples include
     1. Conditions associated with stress and/or pain such as with cancer, dental procedures, dressing changes for burns or wounds, injuries, insertion of IV lines and catheters, fibromyalgia, migraines, neuropathy, and neuralgia.
     2. Helping patients overcome their fear of needles or vaccinations (e.g., a pediatric patient watches an animated story through three-dimensional smart glasses, while a nurse who is watching the story on a separate device simultaneously cleanses the puncture site and draws the blood sample or administers the vaccination).
  3. VR is used therapeutically in patients with dementia and Alzheimer disease, which are often accompanied by symptoms such as agitation, anxiety, apathy, depression, and irritability. The immersive experience appears to stimulate the brain and helps the patient remember things they have forgotten.
  4. VR exposure therapy has been applied successfully in the treatment of numerous psychological conditions including agoraphobia, anxiety disorder (generalized), anxiety disorder (social), arachnophobia, claustrophobia, obsessive-compulsive disorder, panic disorder, and post-traumatic stress disorder. Patients are gradually exposed to the situations they fear. This type of treatment causes cognitive changes to occur over time when the expected associated traumatizing effects no longer occur, sometimes eliminating the need for medications.
  5. VR is used in the treatment of addiction, eating disorders, and autism because of its ability to provide a controlled setting in which patients can develop new coping and communication skills to assist them in recovery from these disorders. Health- and nutrition-related education, visual feedback, and personalized simulations are used to improve self- and body image.
  6. VR headsets are used to reteach cognitive (speech, attention, memory) and motor skills to patients in different types of situations that possibly require extended recovery periods (e.g., concussion, spinal cord injury, stroke, or postamputation phantom limb pain).
  7. VR headsets are also frequently used in music therapy to improve motor, speech, and other cognitive dysfunctions associated with neurological diseases.
  8. XR has proven to be of significant use in health care. It is especially successful in wellness programs and in mental health therapies. Examples include personalized meditation apps for decreasing stress and anxiety, presenting healthier and sicker future versions of a patient to them based on forecasts of their current health habits, and immersion therapy sessions where a patient’s therapist can join a session to teach coping skills and coach the patient through traumatic experiences without being in a real-world situation.
  1. NOTE: The combined technology of XR has a presence in all the previous examples. Newer versions of technology products more frequently involve the features of XR technology.

Wearable, Implantable, and Ingestible Technology

Numerous diagnostic and therapeutic uses

1. Magnetoencephalography (MEG) is a noninvasive method of measuring magnetic fields created from the electrical currents produced by brain activity. When the magnetic field distribution is superimposed on an image of the brain, the functioning sources of activity (e.g., memory, motor activity, speech) can be identified and mapped. MEG is also used to locate and map the area of the brain where epileptic seizures occur, which can improve outcomes for the surgical treatment of seizures. The components of neuroimaging devices are mounted in a wearable headset and include an energy source, detectors, and a computer. The headsets can provide information regarding brain function from patients as they move around. Brain imaging modalities (e.g., EEG, positron emission tomography [PET], fMRI) are relatively advanced in today’s technology, but they have a number of drawbacks that MEG is not affected by and that include expense, portability, and the requirement that the patient remain still during the study.
2. Remote patient monitoring (RPM) using biosensors has been developing since the 1970s and became an even more important tool during the Covid-19 pandemic. The results from RPM can be sent to a smartphone and immediately acted upon by the patient and/or caregiver. They can also be transmitted to the patient’s HCP for discussion at a telemedicine encounter, reducing the need for unnecessary in-person office appointments.
   1. There are a variety of monitoring device designs that employ sensors embedded in arm straps, chest straps, clothing, or wrist straps to collect information. RPM is used to measure, trend, and evaluate various health metrics such as body/skin temperature, blood pressure, hydration, oxygen saturation, pulse, and respiratory rate. They can also be used to monitor activity or movement, monitor body posture, and identify a patient fall.
   2. Technology for cardiac event monitoring has made some advances beyond the standard Holter monitor (circa 1957). Wearable and wireless products allow patients to automatically transmit data (e.g., heart rhythms and RR interval) to their HCP. Some units can be worn continuously for up to 3 wk. There are also event recording devices that can be implanted beneath the surface of the skin and are designed to capture significant but infrequent events that occur over even longer periods of time.
   3. The first capsule endoscopy was introduced in the mid-1990s and is still used today. The capsule contains a camera, light source, radio transmitter, and battery. The patient swallows the capsule, and the camera takes and transmits two images per second. The images are transmitted to a recording device, which saves all images for later review by an HCP. The recording device is approximately the size of a personal compact disk player. It is worn on a belt around the patient’s waist, the video images are transmitted to and stored by the recording device. After 8 hr, the recording device is returned to the HCP for processing. Thousands of images are downloaded onto a computer for viewing by an HCP specialist. The capsule is disposable and excreted naturally in the patient’s bowel movements.
   4. Continuous glucose monitoring systems, inserted under the skin of the abdomen, thigh, or upper arm, accurately measure glucose levels; some systems are integrated with computer-generated calculations for automatic basal and bolus insulin administration. The first system was approved by the FDA in 1999.
   5. Around 2011 the SmartPill, a wireless version of gastrointestinal motility technology, was introduced. The SmartPill is another single-use device resembling a capsule that contains a camera and sensors. The system evaluates digestive function as a measure of transit time through the digestive tract based on measurements of intraluminal pressure, pH, and temperature. The data are sent to a recorder worn either on the patient’s waist with a belt clip or around the neck on a lanyard.
   6. Another smart pill, this one for monitoring a patient’s adherence to their medication regimen, was approved by the FDA in 2017 and is used to monitor compliance for administration of the antipsychotic drug aripiprazole. The system combines therapeutics (a tablet with an implanted sensor) and wearable technology (a wearable patch that receives transmissions from the tablet’s sensor). The patch transfers information to a mobile application, and the patient can track ingestion of the medication on their smartphone. With the proper permissions from the patient, the information can also be shared with caregivers and HCPs through a secure web-based patient portal. Another similar adherance monitoring system is currently under development for the medication tenofovir disoproxil fumarate/emtricitabine. The drug is indicated for preexposure prophylaxis (PrEP) to prevent HIV.
   7. The next-generation type of biosensor is the electronic, e-tattoo, or digital tattoo. These biosensors are made of special conductive inks and ultra-thin electrodes (thinner than a strand of human hair), are able to conform to any shape of the skin, and are more accurate than other wearable biosensors because they are in constant connection with the skin. They can operate without an external power source such as batteries because they are energized by the body’s electrophysiological processes. The tattoos connect wirelessly with a smartphone and are able to send and receive data. Advances in recent research regarding the development of digital tattoos are attributed to improvements in three-dimensional circuit printing technology.

Digital Therapeutics

Digital Therapeutics (DT) is a term that has been used since 2012 to identify the group of health technologies used to manage, prevent, or treat a growing number of physical and mental disorders. DTs are evidence-based therapeutic interventions packaged in high-quality software programs. The focus on health-related interventions has shifted to better treatment and management of chronic conditions. The number of patients with chronic diseases is surpassing the number of patients with acute conditions in our medical system, and as such, treatment programs have had to adjust. Chronic diseases are overwhelmingly behavior mediated. For example, smoking status is the number one health predictor—smoking is addictive, but it is behavioral. Today, behavior change is the first-line treatment for many chronic diseases, including smoking cessation, type 2 diabetes, heart disease, and mental health disorders. The success of DT is based on using methods of encouraging a change in behavior to improve outcomes. DTs may include mobile applications, smartphones, reality technologies, wearable technologies, and artificial intelligence. DTs may be prescribed by HCPs as a stand-alone therapy or in conjunction with traditional methods of treatment such as in combination with drugs. Some DTs are intended to eliminate the need for drugs, without side effects and often with lower costs. They also include human interaction when coaching or other interventions are required.

Lifestyle choices and subsequent responsive behavioral changes are tied to improving overall health and longevity as well as reducing health-care costs. As legal drugs that HCPs administer, the FDA is approving more and more DTs, which clears the way for them to be covered by insurance plans.

• The first DT approved by the FDA in the United States was BlueStar Rx in 2013, an app used to help manage diabetes. BlueStar Rx has demonstrated good results by assisting in the reduction of blood glucose levels to prediabetic measurements. The BlueStar diabetes platform has continued gaining FDA approvals for enhancements of the program. Its ninth approval, obtained in June 2020, gave FDA clearance for the BlueStar Insulin Adjustment Program (for basal insulin titration).
• In 2018 an app named “reSET” was approved by the FDA as the first DT for opioid substance use disorder. reSET delivers the timely (e.g., during times of craving and temptation), on-demand access to advice, information, and cognitive behavioral therapy that patients need to manage their situation—directly on their smartphone. The app has also been approved to treat cocaine and stimulant addictions.
• Other significant examples of the successes provided through DT include eating disorders, insomnia, pain management, smoking cessation, treatment of psychological disorders (e.g., anxiety), and weight loss.

Additive Manufacturing: Three-Dimensional Printing

Three-dimensional printing (3D printing) is a type of additive manufacturing technology that creates detailed three-dimensional objects from computer-assisted designs. The models or other products are formed by adding specific materials, layer by layer until the desired physical object is completed. As mentioned previously, regulation of this technology and products created using 3D printing are regulated by the FDA. However, a new twist on the use of 3D printing has surfaced. Point-of-care applications have come into existence where the products are being created by 3D printers in health-care facilities rather than by being purchased from an FDA-approved manufacturer. FDA guidance documents for manufacturers are available on the FDA website https://www.fda.gov/medical-devices/products-and-medical-procedures/3d-printing-medical-devices. Commonly used applications include the following:

1. 3D printing for making detailed anatomical models from patient imaging (e.g., CT, MRI) studies. The models are used by surgeons to practice and teach surgical procedures; they are also used to plan and perform surgeries with greater accuracy, efficiency, and better outcomes.
2. Creation of patient-specific surgical instruments.
3. Production of patient-specific implants (e.g., arthroplasty components used in knee and hip replacements and spine cages).
4. Production of patient-specific prosthetics.
5. Application of 3D technologies to provide cutting-edge dental care and personalized restoration materials.
6. Development of a blueprint to address supply chain issues (e.g., 3D printed nasal swabs during the Covid-19 pandemic).

Artificial Intelligence and Machine Learning as Medical Devices

The difference between artificial intelligence (AI) and machine learning (ML): AI is technology that enables a machine to simulate human behaviors such as evaluation and analysis of huge data sets, autonomous development of algorithms, followed by implementation of solutions that may evolve over time (e.g., interpretation of imaging studies to create patient-specific models for surgical planning). ML functions through the use of programmed algorithms to process large sets of data, identify patterns, learn from past data without additional programming, and develop solutions for which tasks can be autonomously carried out (e.g., ML used as medical assistant chat bots; if the bot cannot interpret or answer the question being asked, the patient is transferred to a real medical assistant). ML does not involve any type of autonomous decision making as is the case with AI. Deep learning, another type of AI technology, is an application of ML that uses large amounts of data, complex algorithms, and interconnected layers of neural nodes to teach computers how to process data in a manner similar to the human brain.

For the purpose of simplicity, AI will be used to refer to either AI or ML in this appendix. AI and ML are used in numerous software platforms, and each is considered a medical device by the FDA. The FDA publishes and periodically updates a database of FDA-approved AI technologies at https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning -aiml-enabled-medical-devices?.

Examples of AI in medicine include the following:

• Interpretation of diagnostic testing such as CT imaging and microscopic review of tissue samples that are much more accurate than what humans can provide.
• Combinations with other technologies such as with infrared thermography (e.g., no-touch thermometers to help identify people with an elevated temperature), reality technologies, and wearable technologies.
• Analysis of huge banks of data to develop early warning systems regarding the emergence of pandemics or to evaluate and track population trends during a pandemic (e.g., significant increases in depression and suicide during the Covid-19 pandemic).
• The CDC’s website bot to assist users in making decisions regarding appropriate actions to take in cases of suspected or known coronavirus exposure (e.g, isolate, quarantine, seek medical attention, etc.).
• Robotic surgery that has both revolutionized and standardized surgery outcomes. AI-driven robots can also incorporate data from previous surgeries to develop new surgical plans.
• Enhancing the effectiveness of precision medicine by using AI to analyze large amounts of data regarding a particular health condition in combination with rapid genomic sequencing, and biosensors that the patient wears before, during, and after their treatment to collect data on various health-related metrics.
• Bringing about a new level of efficiency in administrative workflow for health-care facilities and provider clinics or offices. AI applications can
  1. Prioritize urgent tasks so the HCP’s time is used effectively and expeditiously.
  2. Automate tasks that are not directly related to patient care using voice-to-text transcription, such as ordering tests, prescribing medications, making appointments, providing pre-appointment reminders and instructions, providing pre- and postprocedure instructions, and recording chart notes.

FDA-approved AI devices are used in laboratory and diagnostic testing applications. Imaging and data analysis are currently the two main areas of study and utilization. Some examples of ML that are being used or studied in clinical laboratories include automated image analysis and reported findings from tissue samples, bone marrow specimens, whole blood films, and urine culture plates. While laboratories have begun to use forms of AI to improve accuracy of results and greater efficiency in timely results reporting (and therefore more opportunities to begin treatment sooner), there are still numerous, significant issues to address before their use becomes widely accepted. An example of a pressing internal challenge lies in developing trust in AI-related technologies that currently rely on the specific education and experience of humans, knowledge that has been accumulated over a long period of time. Significant external challenges to AI becoming a standard tool in laboratories include the development of standardized methods to identify and collect statistically significant, high-quality data from both general and specific patient populations and development of standardized algorithm validation procedures. The ultimate goal is to collect data that will sync health-care processes with preferred outcomes on both a population-based and personalized level. Elimination of biases is necessary to provide more accurate interpretation of results and to address known and unrecognized voids in health-care quality.

Examples of FDA-approved AI devices in the laboratory

• Anatomic Pathology—Paige Prostate is software that uses ML algorithms to assist digital pathology users to scan, review, and evaluate slide images from prostate tissue biopsies. The pathologist reviews the slide on the digital imaging system, the system generates and displays a preliminary classification in a window of the computer screen, and the pathologist makes the final determination—agreement or alternate diagnosis. Pathwork Tissue of Origin Test Kit is a tool that measures the degree of RNA gene expression patterns in a patient sample against a database of known common malignant tumor types diagnosed according to the clinical and pathological practice at the time the database was developed. A computer algorithm estimates the degree of similarity between a patient specimen and the database.
• Cytology—PAPNET Testing System is a similar automated, image recognition tool for cytologists to review cervical tissue biopsies. Specimens with abnormal findings are referred to a pathologist for further review and final diagnosis.
• Hematology—CellaVision DC-1, CellaVision DC-1 PPA, and Scopio X100HT with Full Field Peripheral Blood Smear (PBS) Application are devices that provide a digital scan of the full field of a stained peripheral blood smear monolayer in the feathered edge of blood films at a high (e.g., 100× oil immersion) resolution level. An image recognition tool preclassifies WBCs, provides a platelet estimate and RBC morphology; the technician or technologist reviews the slide on the digital cell counting system, the system generates and displays a preliminary cell differential and description of cellular appearance characteristics in a window of the computer screen, and the tech makes the final determination: agreement or manual review.
• Microbiology—the Apas Independence is an automated culture plate reading system used to screen for urinary tract infections. The Automated Plate Assessment System reviews agar plates from urine samples using imaging technology and sorts the plates into categories (e.g., negative, positive, review) using algorithms derived from a decision tree. The decision tree is based on data from a matrix of morphology characteristics that indicates type of organism from blood and MacConky agar plates and from quantitative colony counts. Negative plates are removed from the workflow and discarded; significant time is saved by eliminating negative plates from the workload. All other plates require further standard work-up by a microbiologist. Results are reported as significant growth, nonsignificant or negative growth; assessment and enumeration of identified bacterial colonies; and patient-specific demographics.
• Urinalysis—the Minuteful kidney test is a home use device for the semiquantitative measurement of urine albumin and creatinine with calculation of the albumin/creatinine ratio, used to assess and monitor kidney health. The system is only available by prescription.
• Information systems/data analysis—Data analysis projects have been initiated to accomplish goals such as predicting laboratory test values based on the results of related laboratory studies (e.g., predicting types of anemia based on findings from a CBC), improving utilization of laboratory resources (e.g., reducing medically unnecessary tests by looking for duplicate orders or by recommending the next tests the HCP should order), increasing consistency of results (AI imaging results are not affected by variation from tech to tech), increasing test result throughput (automating manual processes such as review of microscopic slides that use catalogs of images to classify and enumerate cell types), promoting precision interpretation of laboratory results (based on interpretations provided by huge data sets), and advancing the level of relatable clinical laboratory information by personalizing the interpretation of results in individual or consolidated medical records.

Diagnostic imaging has a wide lead over other medical specialties in the use of FDA-approved AI technology. Decisions regarding the use of AI imaging can be quite fluid depending on the resources available and the individual patient's clinical situation. With AI, the technology can offer significant improvement in the quality and detail of images obtained from common imaging studies such as x-rays to identify abnormal findings. This decreases the need for supplementary testing from more complex and expensive modalities such as MRI. Better imaging with first-line equipment also reduces the amount of time needed to arrive at a diagnosis and begin treatment. In another scenario, use of an advanced study type such as AI-assisted, high-quality MRI could obviate the need for traditionally preliminary studies like x-ray or CT. Last, medical advances have also been achieved through the combination of AI and multiple modalities such as PET-MRI-CT.

AI technology can also be used to automate calculations required for diagnostic purposes (e.g., blood vessel assessment for diameter or changes in blood flow, determination of angle degrees of various structures in the body, monitoring thickening or thinning of bone or muscle).

Examples of FDA-approved AI devices in diagnostic studies

• CT—Point-of-care CT has become more popular since the development of portable imaging devices that deliver lower doses of radiation. Complementary AI technology that rapidly performs segmented measurements and automatically overlays images can compensate for factors that may have altered results obtained from standard CT equipment such as patient movement (e.g., breathing, slight changes in body position) or changes in body composition. The AI software provides high-quality, detailed images in three dimensions, which, for example, are especially important in oncology applications to identify new lesions or changes in lesions.
• MRI—AI can assist a highly skilled MRI technologist to plan complex scans without the oversight of a cardiac imaging provider. AI can also rapidly process images and identify minute abnormalities that may not be detected by the human eye.
• Mammography—Similar to laboratory image recognition tools, AI developed for mammography either identifies and preclassifies abnormalities on mammograms or indicates their absence. The algorithms allow providers to proceed more rapidly through negative or normal cases, which gives more attention to cases with suspicious findings. Studies are underway to compare AI-assisted findings with traditional screening regarding accuracy and improved detection rates.
• Fluorescein Angiography and Optical Coherence Tomography — Adaptive optics (AO) is a new, improved imaging technique, based on optical coherence tomography, that in combination with artificial intelligence (AI) is reported to be 100 times faster and able to provide images that are 3.5–fold better in quality. The improved technology will eventually enable health care providers (HCPs) to identify and evaluate very early development of age-related macular degeneration (AMD) and other retinal diseases.
• Fundoscopy (with photos)—IDx-DR, EyeArt, and AEYE-DS are fully autonomous software programs that analyze images taken by specific fundoscopy cameras. The cameras are designed to provide high-resolution color images of the retina, macula, and optic disc of the eye and securely transmit them to the system's cloud-based platform. The platform's algorithms look for signs of disease or disorders and return a report in less than minutes.

SUMMARY: The Internet of Medical Things (IoMT)

The IoMT can be used to summarize the evolving integration and implementation of technology in health care. The IoMT refers to the wireless interaction between devices and mobile applications that can connect and interact with health-care information systems. The systems collect and analyze enormous amounts of data that are then used to improve health-care outcomes. The IoMT is the place where the development of thousands of health-related apps, new technologies, machine-to-machine communication through WiFi, and an explosion of data storage in the “cloud” comes of age and continues to evolve in real time. In a sense, the Covid-19 pandemic forced the IoMT to more quickly become an integral means to

• Reduce unnecessary office and hospital visits
• Foster collaboration between patient and health-care partners
• Provide remote transfer and tracking of medical information
• Provide treatment for and prevention of chronic diseases in a variety of physical settings
• Introduce widespread use of telemedicine (remote clinical services) as a means for patients and HCPs to communicate remotely
• Expand the use of telehealth to provide both remote clinical and nonclinical services (e.g., interdepartmental meetings, remote education)