“Imagine you’re sitting in a physician’s office waiting to get the results of your tests, and you have no idea what they will say. The wait can be torture. Now imagine your physician has an intelligent system that reviews test results in real time and detects early warning signs of potential issues (5-10 years from now). We’re not talking about something from Star Trek – this is the potential of emerging AI technologies transforming medicine.
Modern healthcare is a sea of information for physicians. Each patient may generate hundreds of lab results, numerous imaging studies (scans), and a lifetime of personal history. While this creates the opportunity for physicians to provide care, the sheer volume of data limits each physician’s ability to make the perfect connection every time. This is why empowering physicians with better tools has become essential.”
The next step will be to use “Transformative” (AI) to assist in decision-making in clinical medicine. When I use the term “Transformative,” I am not thinking about “Robot Doctors.” I am thinking about very capable co-pilots. These “co-pilots” will serve as a second set of highly knowledgeable eyes when reviewing thousands of images or reports to identify relationships and risks that the human eye cannot.
They will also be able to connect multiple seemingly unrelated symptom combinations and present them to the physician for their professional evaluation. When using “Transformative” (AI), we are not replacing a physician’s knowledge; we are augmenting it.
Ultimately, we want to provide your physician with additional time and quality information. With the “heavy-lifting” of data analysis off a physician’s plate, they can spend more time doing what they do best: understanding you, discussing your options, and providing the best medical care possible.
AI for Clinical Decision: How Intelligent Systems Support Doctors
The AI for Clinical Decision-Making (AI CDM) system represents a new generation of medical decision-making. The application of AI for Clinical Decision Making will impact how health care is delivered and help doctors make decisions more quickly and with greater accuracy.
The AI CDM system analyzes large amounts of data and generates clinical insights to support the delivery of quality patient care. Utilizing AI for Clinical Decision Making in the practice of medicine, doctors can leverage advanced algorithms to improve the quality of care provided to their patients while increasing practice efficiency.
The benefits of using AI for Clinical Decision Making include the rapid analysis of complex medical data. For example, AI systems can analyze medical images (e.g., X-rays and MRIs) and identify patterns of disease that may not be apparent to visual inspection.
By rapidly processing large amounts of data, the likelihood of incorrect diagnoses is reduced, allowing doctors to develop treatment plans based on trusted data. The use of AI in clinical diagnosis has increased doctors’ confidence, as it provides access to the most current and accurate data to support their evaluations.
AI for clinical decision-making helps predict patient outcomes using Predictive Analytics. These systems can analyze historical patient data, identify trends that may indicate a health issue, calculate a risk score for each patient, and notify the healthcare provider of potential complications so they can take action. This model improves patient safety while optimizing resource use in healthcare facilities.
AI for Clinical Decision-Making promotes a team-oriented approach among healthcare professionals. It allows healthcare providers to spend more time interacting with patients rather than on routine and administrative work. Healthcare providers can focus on patient-centered care, leading to stronger relationships with patients as they are more involved in their healthcare journey.
AI for clinical decision-making also supports ongoing education in healthcare. As systems for clinical decision-making develop, they continue to improve through learning from additional experience and data. The ability to stay current with the latest studies and clinical practice will improve patient care.
In summary, AI in clinical decision-making has had a significant impact on the healthcare industry. By providing doctors with information-based (data-driven) insights and predictive models, it empowers clinicians to make good decisions for their patients, improves patient safety, and provides an improved way of delivering health care. The future of medicine will be based on integrating human knowledge and experience with artificial intelligence to provide more efficient, effective patient care.
Clinical Decision Support: Turning Medical Data into Action
Clinical Decision Support (CDS) Systems have become integral components of today’s health care system, transforming large volumes of medical data into actionable information. CDS systems that use AI for clinical decision-making help health care professionals obtain the necessary, timely information to support informed decisions about their patients’ care.
The use of AI for clinical decision-making is fundamental for analyzing complex datasets, including patient histories, laboratory results, and medical images. Clinical Decision Support Systems utilize sophisticated algorithms to analyze large volumes of clinical data, identify patterns, and highlight areas that may require additional review or consideration by clinicians. The ability to quickly and accurately respond to identified areas of concern enables clinicians to take action that improves patient care and outcomes.
In addition to improving patient outcomes, Clinical Decision Support Systems enhance the operational efficiency of health care delivery systems. By automating data analysis, CDS Systems reduces clinicians’ cognitive burden, enabling them to focus on delivering high-quality patient care rather than spending excessive time reviewing extensive clinical data. Additionally,
AI for clinical decision-making provides clinicians with the most relevant information in an easily accessible format, enabling them to make evidence-based decisions quickly.
Clinical Decision Support systems (CDS), when integrated into clinical practice, also foster a culture of lifelong learning. CDS are continually evolving and updating based on emerging data, research, and clinical guidelines. In turn, this continuously evolving, improving approach to learning enables clinicians to provide the most current, state-of-the-art evidence-based practices for their patients and to continually improve the quality of care they deliver.
Clinical Decision Support systems, powered by AI for clinical decision-making, are transforming healthcare today. These systems convert medical data into information, or “insight,” from which physicians can make better-informed clinical decisions, manage their workflow, and provide improved patient care. With continued technological advancements, Clinical Decision Support will be a key component of effective and efficient healthcare delivery.
What If Your Doctor Could Read Every Medical Study in Seconds?
We have shown how artificial intelligence can analyze clean data, such as lab tests and heart rate data; however, other parts of your medical records capture much of the humanity of your medical care – the notes your doctor writes during your visits. These types of notes typically include the doctor’s observations of you, what you say to them during your visit, and their own “gut” feelings — all of which are essential for the doctor to make a diagnosis or develop a treatment plan.
Unfortunately, these types of data do not lend themselves well to a standard format that a computer program can easily use and understand. Therefore, reading and understanding this type of language has always been the biggest hurdle for computers when trying to use and interpret the unstructured content of medical records.
However, until recently, this was the case. Recently, a new branch of artificial intelligence has developed the ability to perform a task that is nearly human-like: reading and understanding written language. This area of artificial intelligence is called Natural Language Processing (NLP). Using NLP, an artificial intelligence can quickly review years’ worth of a doctor’s written comments and immediately create a one-page summary of a new patient’s entire medical history.
Therefore, instead of taking a physician 30 minutes to read through a lengthy medical history of a new patient, an artificial intelligence can immediately summarize a patient’s history on one page, and pull out the most critical pieces of information, such as:
• Key diagnoses and past surgeries
• Current medications and allergies
• Family history of disease
This powerful reading skill is also used in other ways besides your files. When you think about how an AI is used in clinical workflow, it is easier to see when you think about a doctor working on a patient who has a unique collection of symptoms that no one has ever seen before. A doctor would have to spend days reviewing medical journals to find similar cases to the one he is currently seeing.
However, with AI, the doctor can simply ask the AI to pull up all medical journal articles worldwide, and the AI will act as a never-ending research assistant, giving the doctor the information he needs in seconds, which is what AI-powered tools enable for quicker diagnosis.
The ultimate goal is to put the entire medical library at your doctor’s fingertips, at the exact time he or she needs it, to help them make decisions. The AI provides the doctor with data and patterns, but the doctor provides the insight, the empathy, and the decision-making power. With the vast amount of information now available, the next step is to go beyond a correct diagnosis and identify the best treatment for you.
AI Clinical Decisions: Enhancing Accuracy and Confidence
AI Clinical Decision-Making has dramatically increased the accuracy and reliability of medical diagnosis and treatment plan formulation in healthcare. The application of sophisticated algorithms and statistical analysis to large volumes of patient-specific data enables healthcare practitioners to make more accurate treatment decisions.
AI Clinical Decision Making’s ability to process large volumes of data in real time is one of its most valuable features. An AI system can rapidly sort through millions of patient medical records, laboratory test results, and images, and identify trends or relationships that may go unnoticed by humans.
As a result, the enhanced analytical capabilities of an AI system will enable physicians to detect potential health problems earlier, provide patients with appropriate care more promptly, and improve patient outcomes. In addition to supporting medical diagnosis, AI Clinical Decision-Making can help develop treatment plans tailored to each patient.
AI Clinical Decisions also increases confidence in healthcare providers’ professional decisions. Using AI for clinical decision-making helps physicians access the most recent evidence-based recommendations by synthesizing the latest medical literature and clinical guidelines.
The incorporation of AI into clinical decision-making helps clinicians build greater confidence in the process and provides them with the information needed to make the best decisions. Therefore, patient care will be more accurate and grounded in the most current scientific literature.
As an additional example of how the continued evolution of AI Clinical Decision Making will improve healthcare delivery by reducing diagnostic error and enhancing quality of care, AI Clinical Decisions foster a collaborative relationship between healthcare professionals and the AI for clinical decision-making.
As these systems evolve, they will foster an environment in which AI for clinical decision-making complements the expertise and experience of healthcare professionals. Ultimately, this collaboration between humans and machines will lead to a more efficient and cost-effective healthcare delivery system that will ultimately benefit both patients and healthcare professionals.
Therefore, AI Clinical Decisions represents a significant step forward in medicine and will improve diagnostic accuracy and clinician confidence in diagnosing and treating patients. Additionally, the use of AI in clinical decision-making will enable healthcare professionals to deliver the highest possible level of care, resulting in improved patient health outcomes.
AI-Driven Healthcare: Smarter Systems, Better Outcomes
AI-Driven Healthcare is transforming modern medicine through AI-enabled smart systems that deliver improved patient outcomes. Through sophisticated algorithms and enhanced data processing capabilities, AI for clinical decision support has empowered healthcare providers to deliver more accurate, efficient care to their patients.
There are several benefits of AI-driven healthcare. The most important advantage of AI-Driven Healthcare is the ability to quickly and efficiently review a large amount of data from a patient’s history. AI for Clinical Decision Support provides clinicians with access to a patient’s complete medical history, laboratory results, and imaging studies.
Clinicians can then identify trends and patterns in this data that may not have been apparent previously, enabling earlier detection of potential health problems. Early identification enables earlier intervention and can significantly improve the quality of care provided to a patient.
AI-Driven Healthcare has been shown to streamline medical practice workflows by automating routine tasks and analyzing large volumes of data, reducing the workload for healthcare providers and allowing more time to interact with and care for patients.
In addition, AI puts clinical decision support at clinicians’ fingertips, enabling them to provide care based on the most current evidence.
The use of AI-Driven Healthcare, in conjunction with healthcare professionals’ expertise, creates a collaborative environment where technology and professional expertise combine to deliver high-quality, personalized treatment plans tailored to each patient’s unique needs. The use of AI in clinical decision-making guides clinicians in diagnosing and treating patients’ conditions, ultimately improving the quality of both the diagnostic process and the treatment plan.
Overall, AI-Driven Healthcare is a key component of the future of medicine, offering the potential to improve patient outcomes, streamline workflows, and enhance the level of care. As the capabilities of AI continue to improve, it is expected that the overall effect on the delivery of healthcare will result in the development of better and more effective treatment options for patients across the globe.
AI for Diagnostics: Detecting Disease Earlier and Faster
AI for Diagnostics will completely change how doctors diagnose and treat disease, enabling earlier and faster detection and treatment of medical conditions. With sophisticated algorithms and machine learning, AI for clinical decision-making improves diagnostic processes, leading to better patient outcomes.
The most significant advantage of AI for Diagnostics is its analysis speed, which enables processing large volumes of data much faster than humans. AI Systems can rapidly scan medical images, laboratory test results, and a patient’s history to identify subtle patterns that may go unnoticed by clinicians. Early detection of serious diseases (cancer, cardiovascular issues, etc.) enables health care providers to intervene early and increase the likelihood of successful treatment.
In addition, AI for Diagnostics assists healthcare professionals by providing evidence-based suggestions and support. AI for Clinical Decision-Making integrates current best practices and clinical guidelines into the diagnostic process, helping clinicians make better-informed decisions and resulting in more accurate diagnoses and more confident clinical judgments.
A key benefit of using AI in Diagnostic decision-making is the reduction in diagnostic errors made by humans. The use of AI for Diagnoses can serve as a second set of eyes, validating diagnoses and alerting clinicians to any abnormalities that require further investigation. Using AI to support clinician decision-making as a team creates fewer opportunities for missed diagnoses and provides patients with appropriate care in a timely manner.
The AI for Diagnoses is changing how health care is practiced through providing earlier and more accurate diagnoses of diseases. By using AI for Clinical Decision-Making, clinicians can improve patient health outcomes, streamline diagnostic processes, and create a more efficient healthcare delivery system. As AI development continues to advance, its contribution to improving diagnostics will lead to better health care for all patients globally.
AI’s Second Pair of Eyes: How It Spots Trouble Sooner on Medical Scans
We’ve all had those moments when we’ve held our breath in a cold room as a machine took an image of what was going on with our internal organs. A radiologist, a doctor with extensive training, will then review the X-ray, CT scan, or mammogram for anything that appears abnormal.
Radiologists are highly skilled; however, like any human, they experience fatigue after viewing hundreds of images per day. In addition, some early signs of disease can be subtle and may appear as only a few pixels on a computer screen. That’s why artificial intelligence (AI) provides such a great second opinion for radiologists.
However, how does an AI tool that can “see” as a skilled radiologist learn? It learns in much the same way as we do – through example. In order to develop these types of tools, researchers provide the AI tool with millions of previously viewed medical images (which were examined by physicians). The researchers then informed the AI which images contained a small, early-stage tumor, and which images were completely normal.
By examining a vast database of labeled examples (a process called “training”), the AI tool learns to identify the unique patterns, textures, and shapes of potential problems.
The end result is a digital assistant that never gets tired. An AI scan analysis can place a soft border around areas requiring closer inspection, drawing the radiologist’s attention to even the smallest anomalies.
In this way, AI in radiology image analysis serves as a second level of review (a safety net) to catch anomalies that a radiologist alone might miss. By providing radiologists with an additional tool to verify their results, AI in medical diagnosis increases accuracy.
AI-powered tools for faster diagnoses in hospitals are not from the distant future; they are already being used alongside radiologists. The AI does not diagnose anything. Instead, it serves as a vigilant co-pilot, alerting the radiologist to areas of concern for further review and determination. What if this same powerful pattern-finding ability could consider not just a single scan but also the patient’s entire medical history to begin connecting the dots and clues?
The Health Detective: How AI Connects the Dots to Predict a Crisis
Although a single scan is a major improvement over trying to find problems, a person’s health story has many chapters. A person’s entire medical history, including every lab test, vital sign measurement, and doctor’s note, is stored in a digital file called an Electronic Health Record (EHR) so a doctor can access it instantly.
For a human doctor, sifting through years of data to identify subtle trends or relationships would be virtually impossible. However, for a computer-based AI, this would be a perfect assignment for a high-tech detective. By continually connecting EHRs to AI, AI will be able to track a patient’s entire record in real time.
In addition to serving as a co-pilot, AI is now a lookout. The AI is no longer simply reviewing past events; it now identifies combinations of small, seemingly unrelated changes that may indicate a developing problem. Consider the analogy of a detective who recognizes that a slight increase in body temperature, combined with a slightly increased heart rate and a particular lab value, creates a hazardous combination.
In other words, this advanced functionality, referred to as Predictive Analytics for Patient Risk Scoring, enables the AI to develop a “Risk Score” for each patient based on their individual characteristics and to alert doctors to patients at greatest risk of developing a problem.
A very good example of this is the battle to treat sepsis, a potentially fatal condition in which the body’s response to an infection becomes uncontrolled. Sepsis develops quickly and, as such, is extremely difficult for doctors to identify in the beginning stages. However, an artificial intelligence (AI) monitoring a patient’s data stream may detect these subtle sepsis warnings hours before the individual does.
Once the AI identifies that a patient’s risk has risen, it gives the doctor a crucial head start on treatment and may mean the difference between a complete recovery and a devastating loss.
The AI will never diagnose or order treatment — it is essentially an intelligent alarm system. Instead, it alerts the nurse or doctor by stating: “It appears the condition of this patient is deteriorating. Please review.” These are some of the reasons why Clinical Decision Support Systems (CDSSs) have the potential to save lives – they provide doctors with a way to act quicker and with more information.
The doctor still verifies the diagnosis and determines the best course of action. The ability of the AI to examine each patient’s data is revolutionary, but what if AI could also interpret all of the medical knowledge in existence?
Beyond a ‘One-Size-Fits-All’ Cure: How AI Helps Tailor Your Treatment
The past few years have shown us how trial-and-error-like the medical system can be at times. Medicine, which works so well for some individuals with certain diseases or conditions, does nothing for others with the same disease or condition, even though both were diagnosed with the same condition.
This is because each of us is uniquely different from birth (i.e., down to our DNA). For many years, the ultimate goal was to move away from the “one size fits all” approach to patient care toward personalized medicine, in which treatment is tailored to your needs. The use of artificial intelligence (AI) in healthcare has made the ultimate goal of personalized medicine a reality.
To understand how personalized medicine is possible through artificial intelligence, consider the following example: when a patient receives standard treatment, it is like buying an off-the-rack suit. Most individuals who buy suits will find an off-the-rack suit suitable for their needs. However, no matter what type of suit an individual buys off the rack, the suit will never perfectly fit every individual’s needs.
To get a suit that perfectly meets an individual’s needs, a person would need to purchase a custom-made suit based on his/her body measurements. Artificial Intelligence serves as the master tailor of healthcare by enabling healthcare professionals to review and analyze thousands of individual data points on each patient’s health history, lifestyle, and genetics to identify the specific clues needed to determine how a particular therapy will affect each individual’s body.
The reason this technology is so powerful is machine learning, which enables the development of personalized treatment plans for individual patients. The AI system analyzes the anonymized data from millions of patients previously treated by the same healthcare providers (the treatments used, the genetic markers associated with each patient, and the outcomes of each treatment).
By analyzing all data associated with each patient, the AI has learned and can now use this information to provide highly accurate predictive insights. For example, when treating patients with cancer, the AI may identify genetic information associated with a particular tumor type and recommend a new, targeted drug that would be unlikely to be effective against tumors with different genetic characteristics. In many cases, this may allow the patient to avoid multiple rounds of traditional chemotherapy.
The example above illustrates the future of AI in patient care. As stated earlier, this does not mean the AI replaces the doctor’s professional judgment. Rather, the AI presents a doctor with a new set of tools to consider.
Those tools are lists of potential treatment options tailored to each patient’s individual needs. The AI provides data-driven solutions for the doctors to evaluate. Ultimately, the doctors will then work with the patient to select the best course of action. However, as these AI technologies continue to evolve, it also raises another unavoidable question: how far will the doctor-AI relationship extend?
Clinical AI Solutions: From Hospitals to Everyday Care
Clinical AI Solutions are at the forefront of changing how healthcare is delivered by bringing together state-of-the-art technology and patients’ real-world needs.
The use of Clinical AI Solutions enhances the efficiency, effectiveness, and accessibility of medical care across all settings – from hospitals to local doctors’ offices and clinics.
One of the main benefits of Clinical AI Solutions is their ability to rapidly analyze large amounts of patient information. Advanced algorithms analyze electronic health records (EHRs), diagnostic imaging, and laboratory test results to identify potential connections or patterns that may indicate a health issue.
The data-driven insights provided by Clinical AI Solutions empower healthcare professionals to make AI-based clinical decisions based on the best available evidence, leading to better patient outcomes.
Clinical AI Solutions have also been adopted in outpatient care via telemedicine and at the primary care physician level. In addition to offering an alternative method for remote monitoring and symptom assessment, Clinical AI Solutions expands healthcare access for individuals regardless of where they live. Therefore, Clinical AI Solutions help remove many obstacles to accessing care, especially for those in underserved communities.
Additionally, clinical AI solutions improve the use of a provider’s time and increase efficiency by automating routine (non-clinical) tasks, such as data entry and appointment scheduling. These solutions reduce administrative burden, giving clinicians more time to provide patient care and less time to complete paperwork.
The potential for future growth of Clinical AI Solutions is tremendous; as they become more sophisticated, they will be able to improve diagnostics, create customized treatment plans based upon individual patient needs, and provide the highest quality of care for each patient. In the future, the combination of Clinical AI Solutions and Healthcare delivery will result in the most efficient, effective, and personalized healthcare system in history.
The Elephant in the Room: Will an AI Ever Replace Your Doctor?
In addition to being incredibly useful for identifying trends, computers cannot replace doctors because they lack two of the three key elements of medicine: wisdom (understanding what makes a difference) and compassion (caring about people beyond their data).
Therefore, instead of replacing doctors, a new and safer way for technology to improve healthcare is emerging. It is often referred to as “Human-in-the-Loop” technology. The Human-in-the-Loop model views the AI as a co-pilot to assist a doctor. It will review millions of pages of medical research or analyze a scan at a level of detail no human could achieve to identify potential problems.
However, the doctor remains in charge and will use the information from the AI in the context of your life and symptoms to reach the ultimate decision regarding your treatment. Therefore, this model pairs the machines’ ability to rapidly and accurately process large amounts of data with a doctor’s expertise and experience to ultimately deliver the best results.
The difference between how AI diagnoses patients and how human doctors assess them is significant. An AI can recognize a statistical chance of a disease; however, an AI cannot sit with you, understand your anxiety, or evaluate which treatment options best fit you personally.
AI cannot observe your non-verbal communication (i.e., body language) and/or have an intuitive sense that there may be other factors at play as well. These uniquely human abilities are the core of medical practice and will ultimately determine the quality of your care.
AI’s role is not to eliminate the human element of healthcare, but to recenter it by eliminating excessive, repetitive data entry/analysis that causes high levels of burnout among doctors. By doing so, AI will allow doctors to spend more time doing what they were trained to do: listening to you, thinking critically about your health, and connecting with you.
The future of AI-assisted patient care does not involve a cold, inhumanized, computerized environment, but rather a doctor who has more time to devote to you. For this relationship to succeed, we need to ensure we can rely on our “co-pilot”.
Can We Really Trust an Algorithm With Our Health? A Look at Safety and Bias
Training a machine learning model on flawed data causes the model to learn and make errors based on its exposure to that data. For instance, a computer can be trained to recognize ‘fruits’, if all images of fruits were apples, then the computer will have difficulty recognizing bananas or oranges.
This is why there is a significant ethical concern about AI in medicine: if an AI system is trained on data from a single demographic group, its accuracy may be lower than expected in other groups.
Concerns about built-in bias mean medical AI systems cannot simply be downloaded and implemented in a hospital setting. Many systems in use today are FDA-regulated, and the FDA also reviews and approves new drugs and medical devices.
Before approval, the FDA reviews how the AI was developed, which data were used to develop it, and how well it performs across different demographics. Reviewing the development process of an AI provides an important layer of protection for patients and helps to ensure that an AI designed to assist patients does not include unknown biases that could lead to patient harm.
Although regulatory checks have been put into place to help make AI-based diagnoses safer, I believe the biggest safety check will still be your doctor. No matter how sophisticated an AI tool is, it will never make a final diagnosis on its own; the relationship between an AI tool’s diagnosis and a human doctor’s expertise will always be a partnership that puts the decision-making power in the hands of the human.
In other words, the AI tool will analyze data, and the doctor will then combine it with a physical exam, lab tests, your personal history, and the doctor’s many years of education/training/intuition to arrive at a final diagnosis. By doing so, you create the potential for a powerful second opinion that never supplants the doctor’s expert judgment in the diagnostic process.
To create a level of trust in AI tools, there will need to be a three-step approach: Developers will need to use diverse and representative data when building the AI tool; Regulators will need to provide rigid oversight of the development of AI tools; Healthcare Systems will need to maintain the option to include a skilled doctor in all interactions with AI tools. If these steps are followed, the ‘co-pilot’ in the exam room will be not only powerful but also reliable, fair, and safe for every patient.
A Glimpse into a Faster, Smarter, More Human Future of Healthcare
Imagining artificial intelligence being used in a hospital environment might have seemed like something out of science fiction – impersonal, difficult to understand, and maybe even a little intimidating. We can now better visualize how these systems serve as highly specialized assistants to doctors.
Your next checkup may look very different from it does today. Before you even enter your doctor’s office, an AI has reviewed your entire medical history and created a one-page summary of the information that is most relevant for your doctor to review. An AI has also pre-analyzed a routine scan you underwent last week and identified a small area where a radiologist should take another look, using their expertise to provide a second set of digital eyes to determine if anything was missed.
With seamless integration of data into their workflow, your physician enters the examination room unburdened by data collection, rather empowered by it. They are freed from the hours of digital documentation and paperwork that are integral to today’s medical practice.
No longer a marathon of collecting as much data as possible before making a diagnosis, the appointment is now a focused, personal conversation about your health, concerns, and the most appropriate course of action. This clearly demonstrates the advantages of Clinical Decision Support Systems (CDS): CDS enables physicians to focus on patients while managing data.
In essence, the story of how transformative AI will impact clinical decision-making is less about the technology itself and more about using it to help reclaim the human aspect of medicine that has been lost over time. Rather than pointing toward a cold and mechanical process of care management in the future of AI, there is hope that technology will work in the background to provide you with more time, more insight, and a better and more trusting relationship with the people who are caring for you.
