In order to effectively learn the capabilities of AI in healthcare, we need to understand it from every perspective. Let's Explore!
The rise of the human population and the massive healthcare data produced in present times are two of the biggest challenges in healthcare. As per United Nations, the human population has grown three times since the 1950s from 2.5 billion to 8 billion in 2022.
If we talk about medical data, as per RCB Capital Markets, 30% of the world data generated belongs to the healthcare industry. Adding to it, the increasing shortage of workforce is also an issue that the industry is handling. In such times, the healthcare industry needs to have strategies and tools to bridge this gap and cater to various use cases that require assistance.
Artificial intelligence in healthcare can automate the majority of back-office operations while managing the data in EHRs (electronic health records). In fact, AI and healthcare have progressed so much that we can gain assistance in medication, nursing, and even surgeries.
Therefore to understand the full spectrum of what AI in healthcare can do? Let’s read this article ahead.
Artificial Intelligence is a term used to describe AI technologies like machine learning, deep learning, neural networks, natural language processing, etc. The potential of computers and other technological devices to mimic human cognition using AI technologies, including the ability to understand, think, and make decisions or conduct actions, is referred to as artificial intelligence (AI).
In light of this, artificial intelligence (AI) in healthcare refers to the use of machines to evaluate and take action on medical data, typically with the goal of predicting outcomes catering to different use cases.
One significant use of AI in healthcare is the use of ML and other conceptual disciplines for clinical diagnosis. By utilizing client data along with other information, AI can help doctors and other medical practitioners make diagnoses and treatment suggestions that are more accurate.
Artificial intelligence trends can help make healthcare more responsive to current diseases. These have anticipatory capabilities by analyzing massive amounts of data to produce better preventive care recommendations for patients.
One major issue in the healthcare industry is the shortage of workforce. It is estimated that the global shortage of healthcare is around 17.4 million. This shortage is fueled because of an aging workforce that is soon to cross the age of 65. Adding to it, the massive data and the rising population increase the problem ten folds.
We’ve been talking about “What is artificial intelligence?” and “What can be AI's possible applications in various fields?” for a very long time. However, this doesn’t mean that the adoption rate for AI has been on the stronger side. It is considering the fact that organizations are aware that AI technologies can effectively put a bandage on the problem to a certain degree (as healthcare still requires a workforce).
Similar to any industry, the healthcare industry is also adopting the tech but at a rather slower pace considering, it is always the early adopters that start a trend. In healthcare, majorly only the big healthcare organizations have either adopted or are thinking of doing it. However, there is still time for AI in healthcare to reach the grass root level globally.
Adding to it, there are so many use cases such as:
These use cases and many others can be made much more efficient and faster using AI in medicine and healthcare. This would enable healthcare organizations to increase their productivity and streamline operations with ease. In fact, the organizations will be capable of providing better care to the patient in larger numbers and reduce costs related to the operations, be it admin or the surgeries.
Note: Want to learn about the types of AI? Here’s an article for you!
There are several AI technologies in healthcare that can help improve the condition of the industry overall. Let’s check them out!
Machine learning is the most important player to understand the role of AI in healthcare. Machine learning is a branch of AI that enables a computer to learn from data fed to it and evolve. The learning mechanism of machine learning is inspired by the human brain. To enforce AI for health, machine learning processes large volumes of clinical data, patient data, and other resources to reinforce its AI model.
Here are some of the benefits of using machine learning in healthcare:
Some of the use cases for which this artificial intelligence in healthcare can be used are:
Natural language processing or NLP is another branch of artificial intelligence that is capable of interpreting human language. This AI used in healthcare has a large dictionary of healthcare data using which it can interpret patient records, healthcare data, surgical reports, etc. It has the capability of interpretation that can be used in collaboration with services like telemedicine, extracting insights from records, providing real-time recommendations, etc.
Here are some benefits of using NLP for medical AI:
Here are some of the use cases of NLP AI in the medical field:
Expert systems are used for solving complicated problems in any industry. These expert systems have a network of “if-else” conditions that helps in finding solutions to the problem. In fact, the first expert system for healthcare was released in the year 1972 called MYCIN. In fact, the history of AI in healthcare starts from it. The system used AI to figure out bacteria that cause severe infections for example bacteremia, meningitis, etc.
Here are some benefits of using expert systems for the healthcare industry:
Below are some of the use cases of expert systems in healthcare:
Other Technologies of AI in Healthcare Market…
There are several ways AI in healthcare can benefit patients. Let’s explore them one by one.
With early detection, the patient can have ample amount of time to seek a proper diagnosis. It can get a hold of all the necessary resources needed to cure the detected disease. Now how can AI help in this?
AI can help to analyze patients' past and present health difficulties. There are AI-driven solutions that increasingly take into account the people's already existing data in the database. The AI then uses the same mechanism to analyze the data set to detect the presence of any disease. Additionally, healthcare practitioners, with the help of AI tools, can diagnose more correctly by comparing the disease specifics.
Numerous healthcare mobile apps' databases have calculated millions of symptoms and diagnoses. More crucially, it can forecast probable health problems that a person may have in the future and thus reduce his/her chance of visiting the ER.
For instance, Google developed the Verily app to predict hereditary and infectious genetic illnesses. With such resources at their disposal, health professionals may accurately anticipate potential hazards in the future and take the necessary precautions now. The predictive analysis has also helped healthcare facilities become known for their improved operational management along with identifying preventable emergency health visits.
If the diagnostic and treatment speed is accelerated, the patient recovers faster, which reduces the chance of him/her going to the ER.
Owing to AI algorithms, medical treatments are now a lot speedier and less expensive. From patient assessment to diagnosis, AI has had a big impact on the treatment’s speed and cost. For instance, AI can discover the biomarkers in our bodies that indicate disease, which can assist in early disease detection. AI technologies have decreased the amount of manual labor necessary to specify these biomarkers. The extensive automation of healthcare has allowed us to respond faster and save more lives.
Not everyone in the medical field is a specialist in every field. But imagine if even a general physician can have access to the technology and knowledge of a specialist, then he/she can help patients who have a particular disease that is beyond the specialization of the doctor. This can be achieved with the help of AI.
AI can also share the competence and expertise of specialists in order to support service providers who might otherwise lack that competence. Popular target fields include radiology and ophthalmology, in part because AI image-analysis methods have long been the subject of research and development. Many systems identify conditions that would otherwise need an ophthalmologist using pictures of the human eye.
A general practitioner, technician, or even a patient could get to that conclusion using these applications. This democratization is important since specialists, especially those with high levels of expertise, are hard to come by compared to the need in many fields.
There are instances when people do not have access to the resources or technology needed to help them with disease detection and treatment. Now, if the system is highly accessible, then everyone has an equal scope of getting the same treatment. This can be achieved with the help of AI. Since healthcare facilities powered by AI are economical and have a greater reach (even to remote areas), the chances of people visiting the ER significantly reduces.
The majority of poor nations struggle to keep up with the rapid global technological breakthroughs and lack access to basic healthcare systems and facilities. The danger of death is greater for citizens of such a nation. WHO claims that the 18.1-year difference in life expectancy between the world's richest and poorest countries as of 2019 is due to poor or no access to healthcare. These underserved locations can benefit from an effective healthcare ecosystem thanks to AI technologies.
Digital technologies with AI support can make patient diagnosis and therapy easier and in time for even the people living in the most remote areas of the world. These applications are present and are specifically designed to aid collaboration between international and national healthcare groups in providing individuals in need with the aid they require.
In robotics applications, artificial intelligence development has advanced significantly. The application of machine learning in surgery is the same. There are specialized AI surgical systems that are 100% accurate and can carry out the smallest movements. This implies that we can do complicated procedures effectively with fewer risks of adverse effects, blood loss, or discomfort. In a similar vein, recuperation from surgery is quicker and simpler.
For instance, while waiting for surgery, patients are exposed to antibacterial nanorobots to get rid of any infections in their bloodstream. The AI-backed information on the patient's current condition that is readily available to surgeons in real time is arguably the greatest component. Patients' concerns, particularly those related to surgery under general anesthesia, have been alleviated because of this.
If regular surgeries are conducted flawlessly, this will decrease the ER visits of patients who have faced complications due to surgery.
If you are wondering how artificial intelligence is used in healthcare, then you should know that this technology is proving to be a game-changer for the sector. Here are a few use cases of AI that will help you get an answer to the question - How can AI improve healthcare?
A number of organizations are developing direct-to-patient systems that will enable triage and advice-giving through phone or chat-based interactions. This offers easy access to fundamental queries and medical problems. For a subset of illnesses, this could provide basic counsel that would normally not be available to populations in rural or under-served locations, helping to reduce increased pressure on primary healthcare practitioners. Although the idea is straightforward, these solutions still require extensive independent validation to demonstrate their patient safety and efficacy.
Diagnosis is an important aspect of detecting the disease and finding a relevant cure for the patient. Medical diagnostics is the process of conducting a diagnosis of a patient. This is ideally done by conducting multiple tests on the patient. Tests such as X-ray, MRI (magnetic resonance imagery), ultrasound, DXAs (bone densitometry tests), biopsy reports, blood tests, etc.
Medical diagnoses via AI truly showcase the potential of artificial intelligence in healthcare. It aids healthcare experts in reaching the conclusion faster. AI algorithms that are used for analysis have the capacity to decode information from medical images such as X-rays, MRIs, DXAs, etc. An AI system for medical diagnostics can help in:
By using AI for medical diagnostics, healthcare practitioners can cross-check their assessment and reach a consensus of providing a relevant treatment faster for the patient.
Artificial intelligence (AI) solutions are being created to find new potential treatments from enormous databases of records on current medications, which might be modified to target serious threats like the Ebola virus. To respond to the threat of fatal diseases, the process of releasing the medication can be accelerated while increasing the effectiveness and rate of drug development.
Management of medical health records is one of the biggest challenges for the healthcare industry as of now. Currently with the boom of big data and increasing healthcare information, the management of data has become a critical problem. There are several types of medical data that are generated by the healthcare systems. Data such as diagnostic reports, medical imaging, multiple tests, allergies, etc. Also with regulations such as HIPAA (Health insurance portability and accountability act), the healthcare ecosystem not only has to ensure the safety of medical data but also preserve it for further assessment. To add to this problem, the type of data generated is primarily of three types:
To solve this problem, AI in healthcare is being utilized to manage the data effectively within the healthcare premises itself. By using technologies such as machine learning, natural language processing, computer vision, etc. the healthcare industry is transforming its medical records management by processing the data via these technologies. These AI technologies in healthcare are promising in terms of the classification of data, analysis of image data, reclassification of existing diseases, reading and analyzing medical codes, etc. By enforcing AI in healthcare for medical record management, the government and the healthcare industry would be able to:
There have already been developments in the assessment of electronic medical records. Previously tonnes of medical data related to cardiovascular diseases and diabetes have been analyzed. Using this data, the industry has been able to predict the incidence of diabetes mellitus with 95% accuracy. Adding to it, they have also been able to predict DLA (disability living allowance) post-cardiac arrest incidence to a degree of 0.913 and 0.948 respectively.
To simplify image analysis and diagnosis in radiology, AI techniques are being developed. By pointing out important areas of interest on a scan to a radiologist, AI can boost efficiency and reduce human error. There is also a chance for fully automated methods, which might comprehend and interpret scans without the need for human assistance, providing rapid interpretation in underserved areas or after office hours. Recent instances of improved tumor detection on MRIs and CT scans serve as an example of the emergence of new chances for cancer prevention.
Robotic surgery or robot-assisted surgery is a medical procedure in which robots are used for small incisions (less invasive) and better magnification, aiding doctors to perform it with more precision and faster.
Right now, robot-assisted surgeries are available for specialties like gastrointestinal, cardiothoracic, gynecologic oncology, otolaryngology (head and neck), and urologic surgery. These surgeries are effective because while conducting surgeries, precision is everything. A surgeon with more stable hands and better visibility is capable of conducting the best surgeries. With these robotic surgeries, surgeons not only get a 3D visualization but precision control as well.
Here are some benefits of robot-assisted surgeries:
These surgeries work by precision movement, surgical instruments, and small high-quality cameras. An operator surgeon makes a small hole or incision into the patient's body. After that using the monitor view conducts the entire surgery.
Covid-19 is one of the best examples of why we need remote patient services and telemedicine. This is one of the best uses of AI in healthcare. These types of services are also ideal for people who are living in remote places, the elderly, and physically-abled people (at times).
Remote patient services and telemedicine have been helping people with figuring out symptoms and diseases. These are some of the common symptoms and diseases that can be detected using AI and healthcare:
Below are the reasons behind the increased interest in telehealth growth and telemedicine:
With the inclusion of artificial intelligence in healthcare, below are the ways via which telehealth and telemedicine have gained an advantage:
AI systems can assist doctors in real-time patient risk identification by analyzing enormous volumes of historical patient data. Re-admission risks and emphasizing patients who have a higher likelihood of returning to the hospital within 30 days of discharge are currently focal points.
Due in part to payers' increasing resistance to paying for hospitalization expenses associated with readmission, numerous businesses, and health systems are already creating solutions based on information from the patient's electronic medical records.
Have you heard of the amazing invention that “ChatGPT” is? Well, there are doctors who have been claiming that the patients visiting them are more well-versed with their issue than before (when they did a Google search).
ChatGPT is not exactly a healthcare chatbot but rather a general-purpose SaaS service that has answers for almost everything. However, it is a possibility that artificial intelligence companies might start using the GPT model for healthcare. However, there are a series of AI-enabled chatbots available for the healthcare industry such as:
Some of the benefits of using these chatbots for the healthcare industry are:
Before the incident of Covid-19, no one had any idea that there can be a new disease that disrupts everything at such a huge scale (from a masses perspective). A disease for which we didn’t have a cure. Labs from all over the world came together and joined hands to create the vaccine. Similar is the case with any new disease but clinical trials are conducted nucleated with other research bodies that are doing the same work. Adding to it, a clinical trial consists of many stages numbered I, II, III, IV, etc. At each of these, loads of clinical data are generated that give way to multiple tasks such as:
With the majority of these tasks, the combination of NLP and ML can be used. This would enable the practitioners to scan relevant data and store it in a central database. Using AI models, this data can be used for extracting insights for fastening the clinical trial. In fact, finding relevant data also becomes much easier. Adding to it, AI can also help in reducing the sample size by fitting relevant candidates for a particular trial. This is done via AI tool by:
Manual errors in human operations are pathological. While the entire human race has been gifted with abstract thinking, sentience, emotions, and pretty much everything that makes us, us. We are not created for endless manual labor because it leads to boredom, and our minds simply start to drift off.
For some tasks, this won’t be a big issue considering a certain level of error is permissible. However patient data is sensitive, and by today’s standard, endless also. The inclusion of manual error in multiple data points of healthcare is collateral. Using AI tools such as document automation, healthcare companies are able to extract and reconcile data accurately and efficiently. Also, these document automation tools have the capability to automatically feed this data to a centralized database. Some examples of such documentation automation tools are hyperscience, rossum, amygb.ai, etc.
Below are some of the most common AI in healthcare tools that are available in the market. These are:
Formerly known as IBM Watson, it is a company owned by Francisco Partners. This company aims at providing products and services for medical facilities, clinical R&D, healthcare analytics, and other healthcare services using AI technology. This company has a range of services such as:
Twill is a healthcare service provider that helps people manage their chronic diseases. It has multiple language support and has been used by over 18 million people. Twill provides multiple services for mind-body health. It provides solutions for its users using AI technology to help them provide a custom healing experience. Some of the services provided by Twill are mentioned below:
Viz.ai is a network of around 1400 hospitals that are available globally. Viz.ai use aritificial intelligence in healthcare services for AI-powered care for medical imaging, CT scans, EKGs, etc., and automate their assessment on the go. There are a series of services that are provided by Viz.ai such as:
Despite the promising potential and a wide range of optimistic analysis, there are few instances of actual AI-enabled solutions being used in clinical practice that creates skepticism around the tech. Let's examine issues and challenges faced with the integration of AI technology in healthcare.
The main challenge that AI faces when it comes to healthcare is in the context of the research conducted to analyze the effects and impacts of AI in healthcare.
There aren't enough established methodologies, prospective trials, or peer-reviewed studies of AI in healthcare. The majority of studies have been retrospective, meaning they have only used historical patient medical records that have been diagnosed. But for physicians to completely comprehend the genuine value of AI treatment and diagnosis software in authentic situations, present patients must be studied over time.
For the clinical and technical validation of AI models, clinicians require high-quality datasets. However, due to the fragmentation of medical data across several Electronic Health records (EHRs) and software platforms, collecting patient information and images to test algorithms is challenging. An important obstacle for AI in healthcare is that the medical data from one organization may not be interoperable with other platforms.
The models may produce incorrect results if the information used to train ML models for AI-powered systems is biased. Data for training models comes from non-stationary contexts like clinics that serve several populations and have changing operational procedures. When an algorithm is developed using data gathered under continually changing circumstances, demography and clinical practice changes can lead to bias.
Regarding the collection of patient data, privacy is a major problem. Although safeguards are in place for researchers to safeguard patient data, nefarious hackers are always trying to gain access. Nobody working with AI should ignore privacy issues if a company as large as Google can have security and medical data vulnerabilities. The ability of the system to anticipate information about patients even when the algorithm had not been supplied with such data would be another way that AI puts privacy at risk.
AI systems are prone to mistakes, which can ultimately result in patient damage or other serious issues. For example, a patient can take a medication that was incorrectly suggested by the AI system, raising extra concerns. The AI-driven radiological scan may also fail to detect malignancy.
An incorrect hospital bed assignment based on AI forecasts can result in accidents and relapses. The potential for wide-ranging consequences of AI-related blunders is their major worry. One mistake could have a negative impact on numerous patients. No family member or friend will also take it lightly if they learn that their loved one experienced a setback due to a computer problem.
The future of AI in healthcare is a promising one. There are multiple areas in which AI has already laid the foundation. All we need is for the market to mature in order for those technologies to become mainstream globally. Here are some technologies that we think will upgrade sooner than we expected.
The future of AI is only limited by imagination. With the help of AI, a lot of things would be possible that were unimaginable before.
Due to various transparency issues, data biases, and privacy protection concerns, advanced AI models aren't yet ready for widespread deployment. However, all of these problems can be fixed by simply looking at the positive side of how AI can help in healthcare. More importantly, the advantages of AI-enabled systems surpass the work required to improve them significantly.
AI in general as a technology is slowly becoming mainstream with each passing moment. Today, we can book appointments, get consultations, check symptoms, and even talk to an AI psychologist, etc. because of AI in healthcare. AI has given the healthcare industry opportunity to uplift itself from the clasp of 24*7 work and reduce the amount of time required to operate on patients. It has decreased the distance between patients and doctors, and their capacity to get recommendations for any ongoing ailment.
With early symptom prediction, drug research, and diagnostics, AI offers great prospects to advance healthcare. The technology enables medical professionals to view the entire picture and consider other diagnoses and treatment choices. Additionally, top healthcare app development companies, in collaboration with existing AI and ML-based technologies, are already assisting businesses in streamlining their workflow and identifying avoidable emergency medical visits. One should be aware of the types of AI, AI use cases, and the future of AI in healthcare to fully reap the benefits of artificial intelligence in healthcare.
There is a company called Hyro that has a conversational AI for healthcare. However going forward with the inception of GPT model, there will be many more healthcare conversational AIs to come.
Some of the major benefits of having AI in healthcare companies are medical diagnostics, accuracy of data processing, and reduction of operational cost.
Some examples of artificial intelligence in the healthcare market are Merative, Regard, Elicit, etc.
Some examples of AI in healthcare are image analysis, drug development, virtual nursing assistants, data management & analysis, etc.
Some examples of AI in healthcare examples are automation of administrative tasks, ML-based radiology, diagnostic process via deep learning, etc.
Aparna is a growth specialist with handsful knowledge in business development. She values marketing as key a driver for sales, keeping up with the latest in the Mobile App industry. Her getting things done attitude makes her a magnet for the trickiest of tasks. In free times, which are few and far between, you can catch up with her at a game of Fussball.
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