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The Use of Artificial Intelligence in Healthcare


Artificial Intelligence in Healthcare

The Use of Artificial Intelligence in Healthcare

  • The artificial intelligence (AI) technologies becoming ever-present in modern business and everyday life are also steadily being applied to healthcare.
  • The use of artificial intelligence in healthcare can assist healthcare providers in many aspects of patient care and administrative processes.
  • Additionally, it is helping them improve upon existing solutions and overcome challenges faster.
  • Most AI and healthcare technologies have strong relevance to the healthcare field, but the tactics they support can vary significantly between hospitals and other healthcare organisations.
  • Moreover, some articles on artificial intelligence in healthcare suggest that the use of artificial intelligence in healthcare can perform.
  • Additionally, it helps in specific procedures, such as diagnosing disease.
  • On the other hand, it will be a significant number of years before AI in healthcare replaces humans for a broad range of medical tasks.
  • But for many, it’s still unclear.
  • What is artificial intelligence in healthcare, and what are the benefits?
  • How is AI used in healthcare today, and what will it look like in the future? Will it replace people in critical operations and medical services one day?

Machine Learning

  • Machine learning is one of healthcare’s most common forms of artificial intelligence.
  • It is a broad technique at the core of many AI and healthcare technology approaches, and many versions exist.
  • Using artificial intelligence in healthcare, precision medicine is the most widespread utilisation of traditional machine learning.
  • Predicting what treatment procedures are likely to succeed with patients based on their make-up and the treatment framework is a giant leap forward for many healthcare organisations.
  • The majority of AI technology in healthcare that uses machine learning and precision medicine applications require data for training, for which the result is known.
  • That is the definition of supervised learning.
  • Artificial intelligence in healthcare that uses deep learning is also used for speech recognition in natural language processing (NLP).
  • Features in deep learning models typically have little meaning to human observers; therefore, the model’s results may be challenging to delineate without proper interpretation.

Types of AI of relevance to healthcare

Artificial intelligence is not one technology but rather a collection of them.

Most of these technologies have immediate relevance to the healthcare field, but the specific processes and tasks they support vary widely.

Some particular AI technologies of high importance to healthcare defined and described below:

Machine learning – neural networks and deep learning

  • The machine learning is a statistical technique for fitting models to data and to ‘learn’ by training models with data.
  • Additionally, machine learning is one of the most common forms of AI.
  • In a 2018 Deloitte survey of 1,100 US managers whose organisations were already pursuing AI.
  • While 63% of companies surveyed were employing machine learning in their businesses.
  • It is a broad technique at the core of many approaches to AI, and there are many versions of it.
  • In healthcare, the most common application of traditional machine learning is precision medicine – predicting what treatment protocols are likely to succeed on a patient based on various patient attributes and the treatment context.
  • The great majority of machine learning and precision medicine applications require a training dataset for which the outcome variable (e.g. onset of disease) is known; this is called supervised learning.
  • A more complex form of machine learning is the neural network.
  • This technology became available in the 1960s, and became well established in healthcare research for several decades.

The more use of AI

  • Additionally, its used for categorisation applications like determining whether a patient will acquire a particular disease.
  • It views problems regarding inputs, outputs and weights of variables or ‘features’ that associate inputs with outputs.
  • On the other hand, it has been likened to how neurons process signals, but the analogy to the brain’s function is relatively weak.
  • The most complex forms of machine learning involve deep learning or neural network models with many levels of features or variables that predict outcomes.
  • There may be thousands of hidden features in such models, which uncovered by the faster processing of today’s graphics processing units and cloud architectures.
  • A typical application of deep learning in healthcare is the recognition of potentially cancerous lesions in radiology images.

Natural language processing

  • Making sense of human language has been a goal of AI researchers since the 1950s.
  • This field, NLP, includes applications such as speech recognition, text analysis, translation and other goals related to language.
  • There are two basic approaches to it: statistical and semantic NLP.
  • Statistical NLP based on machine learning (deep learning neural networks in particular).
  • Additionally, it has contributed to a recent increase in recognition accuracy.
  • It requires a large ‘corpus’ or body of language from which to learn.
  • In healthcare, the dominant applications of NLP involve the creation, understanding and classification of clinical documentation and published research.
  • NLP systems can analyse unstructured clinical notes on patients, prepare reports (e.g. on radiology examinations), transcribe patient interactions and conduct conversational AI.

Rule-based expert systems

  • Expert systems based on collections of ‘if-then’ rules were the dominant technology for AI in the 1980s and were widely used commercially in later periods.
  • In healthcare, they employed for ‘clinical decision support’ purposes over the last couple of decades and are still widely used today.
  • Today, many electronic health records (EHR) providers furnish a set of rules with their systems.
  • Additionally, the expert systems require human experts and knowledge engineers to construct a series of rules in a particular knowledge domain.
  • They work well up to a point and are easy to understand.
  • However, when the number of rules is large (usually over several thousand).
  • Therefore, the authorities begin to conflict and tend to break down.
  • Moreover, changing the rules can be difficult and time-consuming if the knowledge domain changes.
  • They became slowly replaceable in healthcare by more approaches based on data and machine learning algorithms.

Physical robots

  • Physical robots became well known, given that more than 200,000 industrial robots easily installed worldwide.
  • They perform pre-defined tasks like lifting, repositioning, welding or assembling objects in factories and warehouses and delivering hospital supplies.
  • More recently, robots have become more collaborative with humans and are more easily trained by moving them through the desired task.
  • They are also becoming more intelligent, as other AI capabilities are embedded in their brains (operating systems).
  • Over time, it seems likely that the same improvements in intelligence that we’ve seen in other areas of AI would be incorporated into physical robots.
  • Surgical robots initially approved in the USA in 2000 provide ‘superpowers’ to surgeons, improving their ability to see and create precise and minimally invasive incisions, stitch wounds, etc.; human surgeons still make 6 Important decisions.

Robotic process automation

  • This technology performs structured digital tasks for administrative purposes.
  • For example, those involving information systems as if they were a human user following a script or rules.
  • Compared to other forms of AI, they are inexpensive, easy to program and transparent in their actions.
  • Robotic process automation (RPA) doesn’t involve robots – only computer programs on servers.
  • It relies on a combination of workflow, business rules and ‘presentation layer’ integration with information systems to act like a semi-intelligent user of the systems.
  • In healthcare, they still used for repetitive tasks like prior authorisation, updating patient records or billing.
  • When combined with other technologies like image recognition.
  • They became used to extract data from, for example, faxed images to input into transactional systems.

Diagnosis and treatment applications

  • Diagnosis and treatment of disease have been a focus of AI since at least the 1970s when MYCIN developed at Stanford for diagnosing blood-borne bacterial infections.
  • This and other early rule-based systems showed promise for accurately diagnosing and treating disease.
  • On the other hand, it became adopted for clinical practice.
  • Additionally, they were not substantially better than human diagnosticians and poorly integrated with clinician workflows and medical record systems.
  • More recently, IBM’s Watson has received considerable attention in the media for its focus on precision medicine, particularly cancer diagnosis and treatment.
  • Watson employs a combination of machine learning and NLP capabilities.
  • Most observers feel that the Watson APIs are technically capable, but taking on cancer treatment was an overly ambitious objective.
  • Implementation issues with AI bedevil many healthcare organisations.
  • Although rule-based systems incorporated within EHR systems are widely used, including at the NHS, they lack the precision of more algorithmic systems based on machine learning.

Rule-based clinical decision support systems

  • These rule-based clinical decision support systems are challenging to maintain as medical knowledge changes.
  • They often cannot handle the explosion of data and knowledge based on genomic, proteomic, metabolic and other ‘omics-based’ approaches to care.
  • This situation is beginning to change, but it is present mainly in research labs and tech firms rather than in clinical practice.
  • Many of these findings are based on radiological image analysis, though some involve other images such as retinal scanning or genomic-based precision medicine.
  • Since these results based on statistically-based machine learning models, they are ushering in an era of evidence- and probability-based treatment,
  • However, it became generalised and regarded as positive but brings many challenges to medical ethics and patient/clinician relationships.

Patient engagement and adherence applications

  • Patient engagement and adherence have long seen as the ‘last mile problem of healthcare – the final barrier between ineffective and good health outcomes.
  • Although the more patients proactively participate in their well-being and care, the better the outcomes.
  • These outcomes include utilisation, financial results and member experience.
  • Big data and AI are increasingly addressing these factors.
  • Providers and hospitals often use their clinical expertise to develop a plan of care that they know will improve a chronic or acute patient’s health.
  • However, that often doesn’t matter if the patient fails to make the behavioural adjustment necessary.
  • For example, losing weight, scheduling a follow-up visit, filling prescriptions or complying with a treatment plan.
  • Noncompliance: a patient does not follow a course of treatment or take the prescribed drugs as recommended.
  • This is a significant problem.

Survey of more than 300 clinical leaders

  • In a survey of more than 300 clinical leaders and healthcare executives.
  • Additionally, more than 70% of the respondents reported having less than 50% of their patients highly engaged.
  • Moreover, 42% said less than 25% of their patients became highly engaged.
  • If deeper involvement by patients results in better health outcomes, can AI-based capabilities be effective in personalising and contextualising care?
  • There is growing emphasis on using machine learning and business rules engines to drive nuanced interventions along the care continuum.
  • Through information provided by provider EHR systems, biosensors, watches, smartphones, conversational interfaces and other instrumentation.
  • Additionally, the software can tailor recommendations by comparing patient data to different effective treatment pathways. 
  • The guidance can provide to the providers, patients, nurses, call-centre agents or care delivery coordinators.

Administrative applications

  • There are also a great many administrative applications in healthcare.
  • Moreover, AI is less potentially revolutionary in this domain than patient care, but it can provide substantial efficiencies.
  • These are needed in healthcare because.
  • For example, the average US nurse spends 25% of work time on regulatory and administrative activities.
  • Some healthcare organisations have also experimented with chatbots for patient interaction, mental health and wellness, and telehealth.
  • These NLP-based applications may be helpful for simple transactions like refilling prescriptions or making appointments.

AI technology research

  • Another AI technology with relevance to claims and payment administration is machine learning,
  • This could be used for probabilistic matching of data across different databases.
  • Insurers must verify whether the millions of claims are correct.
  • Incorrect claims that slip through the cracks constitute significant financial potential waiting to be unlocked through data-matching and claims audits.

Applications of AI in Healthcare

  • From employing it to detect links between genetic codes, using surgical robots, or even maximising hospital efficiency.
  • Although AI has proven to be a boon for the healthcare industry

Support in Clinical Decisions

  • It’s imperative for health professionals to consider every crucial piece of information while diagnosing patients.
  • As a result, this leads to sifting through various complicated unstructured notes kept in medical records.
  • If a mistake in keeping track of even a relevant fact, a patient’s life could be put at risk.
  • The assistance of Natural Language Processing (NLP) makes it more convenient for doctors to narrow down all relevant information from patient reports.
  • Artificial Intelligence holds the ability to store and process large sets of data to enhance clinical decision support.
  • This technology can be relied upon by doctors for aid in detecting risk factors through unstructured notes.
  • An exciting example is that IBM’s Watson has been employing AI to predict heart failure.

Enhance Primary Care and Triage through Chatbots

  • People tend to book appointments with their GP at the slightest threat or medical issue.
  • This could often be a false alarm or something cured of self-treatment.
  • Artificial Intelligence assists in enabling smooth flow and automation of primary care, allowing doctors to stress over more crucial and dire cases.
  • Saving money on avoidable trips to the doctor, patients can benefit from medical chatbots.
  • Additionally, this is an AI-powered service incorporated.
  • Can help with intelligent algorithms that give patients instant answers to their health-related queries.
  • As well as, it helps with any concerns while guiding them on how to deal with potential problems.
  • These chatbots are 24/7 available and can deal with multiple patients simultaneously.

Robotic Surgeries

  • AI and collaborative robots have revolutionised surgery speed and depth while making delicate incisions.
  • Since robots don’t get tired, the issue of fatigue in the middle of lengthy and crucial procedures is eliminated.
  • AI machines can employ data from past operations to develop new surgical methods.
  • The preciseness of these machines reduces the possibility of tremors or unintended or accidental movements during the surgeries.
  • A few examples of Robots developed for surgeries are Vicarious Surgical.
  • Although it combines virtual reality with AI-enabled robots so surgeons can perform minimally invasive operations.

Virtual nursing assistants

  • AI systems facilitate virtual nursing assistants that can perform various tasks, from conversing with patients to directing them to the best and most effective care unit.
  • These virtual nurses are available 24/7 and can respond to queries, examine patients, and provide instant solutions.
  • The world’s first virtual nurse assistant, Care Angel, can facilitate wellness checks through voice and AI.

Aiding in the accurate diagnosis

  • AI can surpass human doctors and help them detect, predict, and diagnose diseases more accurately and at a faster rate.
  • Likewise, AI algorithms have proved accurate and precise at speciality-level diagnostics and cost-effectively detecting diabetic retinopathy.
  • For instance, PathAI is developing machine learning technology to aid pathologists in making more accurate diagnoses.
  • The company’s current goals include reducing cancer diagnosis errors and developing individualised medical treatment methods.
  • Buoy Health is an AI-based symptom and cure checker that uses algorithms to diagnose and treat illness.
  • Here’s how it works: a chatbot listens to a patient’s symptoms and health concerns and then guides that patient to the correct care based on its diagnosis.

Minimising the burden of EHR use

  • EHRs have played an integral role in the healthcare industry’s journey towards digitalisation.
  • The EHR developers have started using AI to create more intuitive interfaces and automate a couple of the routine processes that consume a great degree of the user’s time.
  • While voice recognition and dictation are helping in enhancing the clinical documentation process, natural language processing (NLP) tools may not go as far.
  • AI can also aid in processing routine requests from the inbox, such as medication refills, and result in notifications.
  • It can also help prioritise tasks that require the clinician’s attention.

What are the Threats of Artificial Intelligence in Healthcare?

Several risks associated with AI in healthcare need to address. Below are a couple of the threats that the Institution’s report had identified:

Errors and Injuries

  • One of the most significant risks that AI in healthcare holds is that the AI system might sometimes be wrong.
  • For instance, if it suggests a lousy drug to a patient or makes an error in locating a tumour in a radiology scan.
  • Which could result in the patient’s injury or dire health-related consequences.
  • AI errors are potentially different for at least two reasons. While mistakes can take place by human medical professionals as well.

Data availability

  • Yet another threat posed by AI systems is that training these systems requires massive amounts of data from multiple sources.
  • This includes pharmacy records, electronic health records, insurance claims records, electronic health, insurance claims, etc.
  • From Data Availability, Bias and Inequality, Privacy Concerns, and shifts in the profession to errors and injuries, there are many threats of AI in Healthcare

Privacy concerns

  • Another area where the employment of AI systems raises this issue is that AI can predict private information about patients even if the patient has never been given the data.
  • For instance, Parkinson’s disease could be detected by an AI system with the trembling on a computer mouse even if the person hasn’t revealed the information to anyone else, which could be considered a violation of privacy by the patient.

Bias and inequality

  • Since AI systems absorb and learn through the data with which they are trained, they can also absorb the biases of the available data.
  • For example, if the data incorporated in AI is mainly collected in academic medical centres.
  • Developing AI systems will have less awareness about, and as a result, will treat patients less effectively from populations that do not typically frequent academic medical centres.

This could lead to shifts in the profession.

  • In the long run, the employment of AI systems could lead to shifts in the medical profession.
  • Particularly in radiology, where most of the work gets automated.
  • This raises the concern that a high degree of employment of AI might lead to a fall in human knowledge and capacity over the years.

Benefits of AI in healthcare

Providing user-centric experiences

  • Using large datasets and machine learning, healthcare organisations can find insights faster and more accurately with AI, enabling improved satisfaction internally and with those they serve.
  • Improving efficiency in operations By examining data patterns.
  • However, AI technologies can help healthcare organisations make the most of their data, assets and resources.
  • Additionally, it is increasing efficiency and improving the performance of clinical and operational workflows, processes, and financial operations.

Connecting disparate healthcare data

  • Healthcare data is often fragmented and in various formats.
  • By using AI and machine learning technologies, organisations can click disparate data to get a more unified picture of the individuals behind the data.

AI in healthcare use case: Natural language processing

  • When subject matter experts help train AI algorithms to detect and categorise specific data patterns that reflect how language is used in their part of the health industry.
  • This natural language processing (NLP) enables the algorithm to isolate meaningful data.
  • This helps decision-makers find the information they need to make informed care or business decisions quickly.

Healthcare payers

  • For healthcare payers, this NLP capability can be a virtual agent using conversational AI to help connect health plan members with personalised answers at scale.
  • View the resource.

Clinical operations and data managers

  • Clinical operations and data managers executing clinical trials can use AI functionality to accelerate searches and validation of medical coding.
  • This can help reduce the cycle time to start, amend, and manage clinical studies.
  • Some AI-powered clinical decision support tools feature natural language processing and domain-based training 
  • A typical clinical study can produce vast datasets containing thousands of images, leading to incredible amounts of data needing review.
  • Using AI algorithms, reflections from the healthcare industry can be analysed for patterns and hidden relationships, which can help imaging professionals find critical information quickly.

How can AI in healthcare support health equity?

  • The healthcare IT industry is responsible for creating systems that help ensure fairness and equality in data science and clinical studies, which leads to optimal health outcomes for everyone.
  • AI and machine learning algorithms can be trained to help reduce or eliminate bias by promoting data diversity and transparency to help address health inequities.
  • For example, minimising bias in healthcare research can help combat health outcome disparities based on gender, race, ethnicity or income level.

Top 11 Ways Artificial Intelligence Will Impact Healthcare

1. Unifying Mind And Machine Through Brain-Computer Interfaces

  • Neurological diseases and trauma to the nervous system can take away some patients’ abilities to speak, move, and interact meaningfully with people and their environments.
  • Brain-computer interfaces (BCIs) backed by artificial intelligence could restore those actual experiences to those who feared them lost forever.
  • “If I’m in the neurology ICU on a Monday, and I see someone who has suddenly lost the ability to move or to speak, we want to restore that ability to communicate by Tuesday,” said Leigh Hochberg, MD, PhD, Director of the Center for Neurotechnology and Neuro recovery at MGH.

2. Developing The Next Generation Of Radiology Tools

  • Radiological images obtained by MRI machines, CT scanners, and x-rays offer non-invasive visibility into the human body’s inner workings.
  • But many diagnostic processes still rely on physical tissue samples obtained through biopsies, which carry risks, including the potential for infection.
  • Artificial intelligence will enable the next generation of radiology tools that are accurate and detailed enough to replace the need for tissue samples in some cases, experts predict.
  • We want to bring together the diagnostic imaging team with the surgeon or interventional radiologist and the pathologist,” said Alexandra Golby, MD, Director of Image-Guided

Neurosurgery research at Brigham

  • Neurosurgery at Brigham; Women’s Hospital (BWH). “That coming together of different teams and aligning goals is a big challenge.”
  • “If we want the imaging to give us information that we presently get from tissue samples.
  • Then we’re going to have to be able to achieve very close registration so that the ground truth for any given pixel is known.”
  • Succeeding in this quest may allow clinicians to understand better how tumours behave as a whole instead of basing treatment decisions on the properties of a small segment of the malignancy.
  • Providers may also better define the aggressiveness of cancers and target treatments more appropriately.
  • Artificial intelligence is helping to enable “virtual biopsies” and advance the innovative field of radionics, which focuses on harnessing image

3. Expanding Access To Care In Underserved Or Developing Regions

  • Shortages of trained healthcare providers, including ultrasound technicians and radiologists, can significantly limit access to life-saving care in developing nations worldwide.
  • More radiologists work in the half-dozen hospitals lining the renowned Longwood Avenue in
  • Boston than in all of West Africa, the session pointed out.
  • Artificial intelligence could help mitigate the severe deficit of qualified clinical staff by taking over some of the diagnostic duties typically allocated to humans.
  • For example, AI imaging tools can screen chest x-rays for signs of tuberculosis, often achieving a level of accuracy comparable to humans.
  • This capability could be deployed through an app available to providers in low-resource areas, reducing the need for a trained diagnostic radiologist on site.

Algorithm developers in the US

  • Reducing The Burdens Of Electronic Health Record Use
  • EHRs have played an instrumental role in the healthcare industry’s journey towards
     digitalisation, but the switch has brought myriad problems associated with cognitive overload, endless documentation, and user burnout.
  • EHR developers are now using artificial intelligence to create more intuitive interfaces and automate some of the routine processes that consume so much of a user’s time.
  • Artificial intelligence may also help process routine requests from the inbox, like medication refills and notifications.

4. Containing The Risks Of Antibiotic Resistance

  • Antibiotic resistance is a growing threat to populations worldwide as overuse of these critical drugs fosters the evolution of superbugs that no longer respond to treatments.
  • Multi-drug resistant organisms can wreak havoc in the hospital setting and claim thousands of lives yearly.
  • C. difficile alone accounts for approximately $5 billion in annual costs for the US healthcare system and claims more than 30,000 lives.
  • Electronic health record data can help identify infection patterns and highlight patients at risk before they begin to show symptoms.
  • Leveraging machine learning and AI tools to drive these analytics can enhance their accuracy and create faster, more accurate alerts for healthcare providers.

5. Creating More Precise Analytics For Pathology Images

  • So, the more accurate we get, and the sooner we get to the correct diagnosis, the better we will be.
  • That’s what digital pathology and AI have the opportunity to deliver.”
  • Analytics that can drill down to the pixel level on huge digital images can allow providers to identify nuances that may escape the human eye.

How AI helps in patient treatment

  • Artificial intelligence can also improve productivity by identifying features of interest in slides before a human clinician reviews the data.
  • “AI can screen through slides and direct us to the right thing to look at so we can assess what’s important and what’s not.
  • That increases the efficiency of the pathologist’s use and the value of the time they spend for each case.”

6. Bringing Intelligence To Medical Devices And Machines

  • Smart devices are taking over the consumer environment, offering everything from real-time video from the inside of a refrigerator to cars that can detect when the driver is distracted.
  • In the medical environment, smart devices are critical for monitoring patients in the ICU and elsewhere.
  • Using artificial intelligence to enhance the ability to identify deterioration.

7. Advancing The Use Of Immunotherapy For Cancer Treatment

  • Immunotherapy is one of the most promising avenues for treating cancer.
  • By using the body’s immune system to attack malignancies, patients may be able to beat stubborn tumours.
  • However, only a small number of patients respond to current immunotherapy options.
  • Additionally, the oncologists still do not have a precise and reliable method for identifying which patients will benefit from this option.
  • Computational Pathology and Technology Development at the MGH Center for Integrated
  • Diagnostics. “But we still don’t understand all of the disease biologies.
  • This is a very complex problem.”
  • “We need more patient data. The therapies are relatively new, so few patients have been put on these drugs.
  • So whether we need to integrate data within  one institution or across multiple institutions will be a critical factor in augmenting the patient population to drive the modelingmodellingmodeling modelling process.”

8. Turning The Electronic Health Record Into A Reliable Risk Predictor

  • EHRs are a goldmine of patient data, but extracting and analysing that wealth of information in an accurate, timely, and reliable manner has been a continual challenge for providers and developers.
  • “You might hear that an algorithm can predict depression or stroke, but when you scratch the surface.
  • Relying on MRI results might appear to offer a more concrete dataset, he continued.
  • “But now you have to think about who can afford the MRI and who can’t? So what you end up predicting isn’t what you expected.
  • You might be predicting billing for a stroke in people who can pay for a diagnostic rather than some cerebral ischemia.”
  • But ensuring that those algorithms do not confirm hidden biases in the data is crucial for deploying tools that will genuinely improve clinical care, Obermeyer maintained.
  • “The biggest challenge will be making sure exactly what we’re predicting even before we start opening up the black box and looking at how we’re predicting it,” he said.

9. Monitoring Health Through Wearables And Personal Devices

  • Almost all consumers now have access to devices with sensors that can collect valuable data about their health.
  • From smartphones with step trackers to wearables that can track a heartbeat around the clock, a growing proportion of health-related data is generated on the go.
  • Artificial intelligence will play a significant role in extracting actionable insights from this large and varied treasure trove of data.
  • But helping patients get comfortable with sharing data from this intimate, continual monitoring may require a bit extra work, says Omar Arnaout, MD, Co-director of the Computation
  • Neuroscience Outcomes Center and an attending neurosurgeon at BWH.
  • However, patients tend to trust their physicians more than they might charge a big company like
  • Facebook, he added, may help ease any discomfort with contributing data to large-scale research initiatives.

 

10. Making Smartphone Selfies Into Powerful Diagnostic Tools

  • The quality of cell phone cameras is increasing every year and can produce images that are viable for analysis by artificial intelligence algorithms.
  • Dermatology and ophthalmology are early beneficiaries of this trend.
  • Researchers in the United Kingdom have even developed a tool that identifies developmental diseases by analysing images of a child’s face.
  • The algorithm can detect discrete features, such as a child’s jawline, eye and nose placement, and other attributes that might indicate a craniofacial abnormality.
  • Currently, the tool can match the ordinary images to more than 90 disorders to provide clinical decision support.
  • Using smartphones to collect images of eyes, skin lesions, wounds, infections, medications, or other subjects may help underserved areas cope with a shortage of specialists while reducing the time to diagnose specific complaints.

11. Revolutionising Clinical Decision Making With Artificial Intelligence At The Bedside

  • Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics.
  • AI can provide earlier warnings for conditions like seizures or sepsis, often requiring intensive analysis of highly complex datasets.
  • Machine learning can also help support decisions around whether or not to continue care for critically ill patients, such as those who have entered a coma after cardiac arrest, says Brandon
  • Typically, providers must visually inspect EEG data from these patients.
  • The process is time-consuming and subjective, and the results may vary with the skill and experience of the individual clinician.
  • But trying to see if it changed from ten seconds of data taken 24 hours ago is like trying to look if your hair is growing longer.”
  • By powering a new generation of tools and systems that make clinicians more aware of nuances.
  • More efficient when delivering care and more likely to get ahead of developing problems.
  • AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.

Conclusion:

  • The artificial intelligence (AI) technologies becoming ever-present in modern business and everyday life is also steadily being applied to healthcare.
  • The use of artificial intelligence in healthcare has the potential to assist healthcare providers in many aspects of patient care and administrative processes.
  • It is helping them improve upon existing solutions and overcome challenges faster.

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