Artificial Intelligence in Healthcare – Applications, Uses and Challenge

Modern business and everyday life are increasingly reliant on artificial intelligence (AI) technologies, which have also steadily made their way into healthcare. A wide range of patient care and administrative processes can be assisted by artificial intelligence in healthcare, enabling providers to improve upon existing solutions and successfully overcome challenges.

Healthcare organizations and hospitals can use AI and healthcare technologies to support a wide range of tactics, but these tactics can vary greatly. The use of artificial intelligence in healthcare has been suggested in some articles that it is just as capable or even better than humans at certain procedures, such as diagnosing diseases, but a significant number of years will pass before artificial intelligence can completely replace humans in healthcare.

However, it remains unclear for many. How does artificial intelligence in healthcare benefits patients? How will artificial intelligence affect healthcare in the future, and how is it currently used? One day, will it replace people in key medical and operations roles?

In this article, we’ll explore some of the points of artificial intelligence and how it can prove beneficial for the healthcare industry.

Applied AI in healthcare includes the administration of medical services and the delivery of those services. Robotics, natural language processing (NLP), and machine learning (ML) are used in numerous areas of healthcare.

However, along with many potential benefits, this technology also poses many potential concerns – including the misuse of patient data resulting from centralization and digitization, and possible cross-linkage with nanomedicine or universal biometric identification. The technology may also be able to improve healthcare equity by addressing equity and bias issues in some early AI applications.

Artificial intelligence is being used more and more widely in the healthcare industry, despite its relatively new deployment.

Consequently, healthcare AI has a wide range of potential applications, from administrative to medical.

In the healthcare industry, AI has proved to be a boon, whether it be used to detect links between genetic codes or to put surgical robots to work.

A significant portion of total healthcare costs is attributed to administrative expenses, which are estimated at 15-25 percent. Insurers, payers, and providers all benefit from tools that streamline and improve administration.

While healthcare fraud can occur at many levels and be committed by a variety of parties, identifying and curtailing it may provide the most immediate return. Insurers can receive bills for services that were never rendered or surgeons to perform unnecessary procedures to obtain higher insurance payments due to fraud. A defective device or test kit may also be billed to an insurer.

It is possible to stop fraud before it happens with the help of artificial intelligence. A health insurer can use algorithms in the same way that banks do to detect unusual transactions.

Data management is the most widely used application of artificial intelligence and digital automation in health care since the first step is compiling and analysing information (like medical records). The use of robots allows data to be collected, stored, re-formatted, and traced faster and more consistently.

A health professional must consider all important information when diagnosing a patient. Therefore, this requires sifting through unstructured medical records that are complicated and unorganized. A patient’s life could be at risk if even a single relevant fact is overlooked.

By using Natural Language Processing (NLP) in patient reports, doctors can concentrate on all pertinent information more easily.

Data processing and storage capabilities of artificial intelligence allow for knowledge databases to provide and facilitate an individual patient’s examination and recommendation.

To help in selecting the correct, individually customized treatment path, artificial intelligence systems analyse data – notes, reports, and external research – in a patient’s file.

A chatbot can be used by hospitals to check on patients and give them information more quickly. A 94% engagement rate was achieved among oncology patients at Northwell Health after implementing patient chats. The tool extended the range of care provided by clinicians who tried it. Symptoms and recovery can be checked by chatbots. Chatting by text is also becoming more popular, which means more people are adopting it. Furthermore, chatbots help patients navigate the healthcare system more easily. Hospitals and clinics can be found using these websites, and people can book appointments describing their needs.

The number of patients who fail to follow their prescriptions may even reach half. AI can, however, improve the chances of patients taking their medication as prescribed. A few platforms suggest to health professionals what channels they should use to engage patients about compliance. It is possible to use chatbots to remind patients to take their medications. An example of how Artificial Intelligence (AI) helped researchers find the best diabetes medications is presented above. Over 83% of patients were helped by these algorithms, even if they needed multiple medications at the same time.

EHRs have the potential to cause user burnout, cognitive overload, and endless paperwork as a result of their transition to digitalization. A number of routine processes that consume a great deal of user time have already been automated by EHR developers using AI.

NLP tools, though beneficial for clinical documentation, may not be as effective as voice recognition and dictation. Artificial intelligence can also help respond to routine requests, such as requests for refilled medications. Moreover, it can simplify the operation of to-do lists for users by allowing them to prioritize tasks that need their attention.

The application of AI to chronic diseases like cancer and radiology is enabling efficient and precise inventions that can assist in the treatment of patients suffering from these conditions and help find a cure. In comparison to traditional methods of making clinical decisions and analytics, AI offers several advantages. Because AI algorithms get the opportunity to study training data, they make the systems more accurate, which helps humans understand treatment variability, care processes, diagnostics, and patient outcomes like never before.

Artificial Intelligence and Machine Learning tools offered by PathAI allow pathologists to diagnose accurately. A range of new treatments for cancer can be offered using PathAI to reduce errors during the diagnostic process. As cancer diagnosis accuracy increases, more cancer patients can be treated or cured at an earlier stage before their disease turns fatal, saving many lives.

Healthcare facilities are becoming increasingly scarce, and maintaining good overall patient care becomes harder.

The worst part of the patient experience, according to a recent study, is poor communication. These are some ways in which artificial intelligence can assist in overcoming this challenge:

A lack of medical professionals, including ultrasound technicians and radiologists, restricts access to lifesaving treatments in developing nations.

Longwood Avenue in Boston boasts more radiologists than all of West Africa combined, according to the session.

Some of the diagnostic duties traditionally performed by humans could be taken over by artificial intelligence to alleviate the effects of this severe shortage of qualified clinical staff.

Imaging tools based on artificial intelligence can, for example, screen chest X-rays for tuberculosis signs with accuracy comparable to human ability. Providing low-resource areas with this capability could be done through an app, reducing the need for on-site doctors trained in diagnostic radiology.

To realize the potential of this technology in precision medicine, AI algorithms need to be able to analyse vast amounts of information quickly. The current diabetes rate in the United States is 11.3%. Using an AI-based real-time glucose monitoring system, healthcare professionals can understand the disease and treat it more effectively.

As AI technologies develop, they are being used to diagnose a variety of diseases in the healthcare industry. These technologies include support vector machines, neural networks, and decision trees. By using Medical Learning Classifiers (MLCs) and mass electronic health records, Artificial Intelligence (AI) can significantly assist doctors in diagnosing patients.

The application of artificial intelligence in medical facilities helps intake professionals. As part of a Stanford University pilot project, algorithms are used to determine if patients are at risk of needing ICU care or requiring rapid response teams after experiencing code-related events. Using those tools, physicians can more easily determine if certain events are likely to occur within six to 18 hours. A nurse can use AI-based nursing applications to enhance decision-making, receive information regarding patient needs, and receive robotic assistance in dangerous or challenging situations.

Artificial intelligence also automates time-consuming and redundant tasks in healthcare through its tools. This allows administrators to work on other important and necessary tasks while they have some spare time. A platform based on artificial intelligence (AI) called Olive automates several tasks, such as determining whether unadjudicated medical claims are eligible, transferring the necessary medical data to the appropriate medical professionals, etc.

Reducing the burdens of electronic health record use. As Olive integrates easily into existing tools and software, hospitals do not have to spend money on costly downtime.

Several medical software products have been developed based on artificial intelligence that offers personalized and interactive healthcare services such as consultations with doctors whenever it is convenient for them. By using AI chatbots, patients can access hospitals more easily and efficiently. The patients are automatically recommended the appropriate medication if the issues are minor, and if a visit to the doctor is necessary, the same is recommended.

Artificial intelligence is delivering safer and smarter surgeries through healthcare robotics. In complicated surgical procedures, robotic-assisted surgery allows surgeons to feel more precise, safe, flexible, and in control.

Technology also allows surgeons to perform remote surgery from faraway locations where they may not have access to surgeons. It is also necessary to maintain social distance during global pandemics.

In a study published by Harvard Medical School, robot-assisted prostate cancer surgery was compared to conventional prostate cancer surgery. Patients who underwent surgery with robot assistance also had shorter hospital stays, lower pain scores after surgery, and fewer postoperative complications including blood clots, urinary infections, and bladder neck contractures.

There are many challenges associated with machine learning, as well as imperfect laws and ethics, as well as low social acceptance of AI. AI in healthcare faces the following challenges:

To validate AI models clinically and technically, clinicians require high-quality datasets. It is challenging to collect patient information and images to test AI algorithms due to fragmented medical data across multiple EHRs and software platforms. Interoperability issues may also make medical data from one organization incompatible with another. Medical data must be standardized for AI systems to be tested on a large amount of data.

There aren’t always generalizable metrics for measuring AI models’ performance. In clinical practice, however, AI tests are often not accurate enough to determine clinical efficacy despite their technical accuracy. This is known as the AI chasm.

Developed AI algorithms should be evaluated jointly by clinicians and developers before they are considered for clinical use. Decision curve analysis can be used to evaluate models in terms of accuracy and clinical relevance. Identifying threshold probabilities for different categories of patients will require additional research for clinics. Some of the AI models may be inaccurate.

Healthcare experts noted that the county council’s internal capacity for managing strategic change will make implementing AI systems challenging. Experts need to emphasize the need for infrastructure and joint ventures with familiar structures and processes to promote abilities to work with AI implementation strategies at the regional level. This action was necessary to achieve organizational goals, objectives, and missions.

Since change is a complex process, healthcare professionals are only partially responsible for implementing it. CFIR emphasizes organization capabilities, climate, cultures, and leadership as key factors in the “inner context,” which play a role in implementing strategies. The capacity to implement innovations in healthcare depends on the maintenance of a functioning organization and delivery system.

AI helps reduce medication costs by helping to diagnose, predict, and treat diseases more accurately. As part of their work, the researchers are also developing an artificial intelligence project that could prove helpful for humans in the near future. Patients with physical disabilities or spinal cord injuries can also benefit from brain-computer interfaces.

It is therefore time for some major changes in the healthcare industry. New AI-based technologies can be applied to chronic diseases, cancer, radiology, and risk assessment with greater precision, efficiency, and cost efficiency. In the healthcare industry, AI can be implemented by making parallel changes to the environment in a manner that is bound by legal, ethical, economic, and social constraints.