Artificial Intelligence in Health

By: Dzhingarov

AI provides healthcare organizations with an efficient means of streamlining operations and improving patient experiences, freeing medical professionals to focus on providing direct, compassionate face-to-face professional care.

Artificial intelligence also enables healthcare providers to detect diseases like PH and cardiac amyloidosis earlier, which can reduce delays in treatment and potentially save lives. Read on to gain more insight into how artificial intelligence is driving healthcare forward.

Medical Imaging

Medical imaging is one of the cornerstones of healthcare, playing an essential role in improving diagnostics, treatment and outcomes for patients. Unfortunately, however, interpreting computed tomographies (CT scans) or magnetic resonance imaging (MRI) images requires extensive and tedious human labor; using AI could automate this work and increase efficiency for radiologists and clinical staff – leaving them more time to spend time with their patients.

AI can identify patterns or abnormalities not immediately obvious to humans, making AI especially effective at analysing large datasets like CT or MRI images and helping doctors detect early signs of disease, as well as any complications that could be avoided with prompt interventions.

One such AI application developed recently can detect large vessel obstruction – an early indicator of stroke – in medical images, alerting both patient and doctor on a priority basis when necessary to take prompt action.

An AI system has also been employed to predict heart rhythm disturbances such as atrial fibrillation based on analysis of remote electrocardiogram (ECG) data. This allows doctors to quickly and easily identify patients requiring immediate care and thus enhance cardiac outcomes.

AI in healthcare can also assist in the detection of fraudulent insurance claims, which costs nearly $400 billion each year and drives up premiums and out-of-pocket expenses for consumers. AI can detect unusual or suspicious patterns in claim data that signal potential fraudulent activity such as false billing practices, unbundling (charging separately for individual steps of procedures), or unnecessary tests.

AI technology can also be utilized to analyze historical claim data and identify which patient populations are more at risk of having their insurance claim denied or underpaid, which will enable hospitals to detect billing errors more efficiently while improving financial performance – an especially helpful function in today’s healthcare budget-constrained world.

Drug Discovery

AI is revolutionizing drug discovery and development by helping researchers develop more effective approaches to combating deadly diseases. AI allows researchers to analyze massive data sets and quickly identify promising molecules, potentially cutting the time required for creating new drugs by up to 10 times, as well as increasing sales for pharmaceutical companies.

AI’s machine learning (ML) algorithms are also being applied to clinical trials in order to predict drug safety and effectiveness, helping pharmaceutical producers increase successful trials while cutting costs associated with failed drugs.

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AI is revolutionizing healthcare services by freeing physicians up from tedious administrative duties like documentation support and medical scribe services so they can focus more time on providing superior patient care. By automating repetitive processes like this one, AI allows doctors to devote more time and attention to providing care that truly meets patient needs.

AI can also assist healthcare organizations in detecting and preventing fraud, which costs the health industry an estimated annual $380 billion. AI can reduce this figure by identifying suspicious patterns in insurance claims such as unbundling (when hospitals bill for multiple steps of an operation separately) or performing unnecessary tests to take advantage of increased reimbursements.

Artificial intelligence (AI) can also help streamline operations and produce more precise patient outcomes, for instance helping surgeons make more accurate surgical decisions based on real-time imaging of the body, helping reduce invasive procedures, reduce post-surgery pain and speed recovery periods. AI could also monitor patient symptoms to alert healthcare professionals of any changes that require hospitalizations – this could reduce hospital stays as well as potentially saving lives from conditions like sepsis or heart attacks going undetected.

AI technology can also be applied outside hospital walls by analysing data collected via medical-grade wearable devices at home and in the community, for example by remotely reading electrocardiogram readings remotely taken of individuals to detect irregularities indicating atrial fibrillation – an illness affecting millions each year that often goes undetected.

Clinical Trials

AI technologies could transform clinical trials by providing superior algorithms, increased data access and cheaper hardware – potentially making them more efficient and cost-effective than ever. AI can assist by sorting through massive amounts of patient records, genomic information and clinical trial results in order to identify eligible patient populations.

To do this effectively, data analysis involves gathering multiple sources and employing natural language processing (NLP) and machine learning technologies to automate tasks that would otherwise require human effort. For instance, NLP can quickly parse unstructured text found in electronic medical records and patient-reported outcomes to quickly identify patterns or correlations, saving both time and resources in the process.

Other uses for artificial intelligence in clinical trial design include helping researchers optimize clinical trial design. A recent study, for instance, demonstrated that using AI to sift through social media data can speed cohort identification, enabling researchers to test more hypotheses more quickly. Furthermore, using this approach could reduce sample size requirements while simultaneously increasing chances of identifying patients who meet certain characteristics – all key elements in deciding whether a drug works or not.

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AI can also play an invaluable role in helping detect adverse events. By analyzing multiple sources of data such as electronic health records, patient-reported outcomes and social media channels simultaneously, AI can more quickly identify potential safety concerns than traditional methods – helping ensure patients are monitored correctly while also helping ward off potentially serious incidents and saving time.

AI can also assist healthcare organizations in combatting healthcare fraud, which costs the industry $380 billion/year and causes insurance premiums and out-of-pocket expenses for consumers to increase significantly. AI software is capable of detecting unusual or suspicious patterns in insurance claims such as billing for services not performed, unbundling charges for individual parts of an operation and conducting unnecessary tests in order to take advantage of insurance payments.

But obstacles remain for incorporating AI into clinical trials, particularly concerning data interoperability, inherent bias and privacy concerns. Furthermore, it’s vital that AI systems be tested against any existing or new technology designed to complement or replace them before creating guidelines on how best to oversee and use these new tools.

Patient Monitoring

Artificial intelligence holds immense promise to transform healthcare. Not only will medical professionals benefit from AI technology’s innovative applications; patients too can gain more control of their own healthcare through more precise and timely data.

Rapid identification and response of potential risks is vital to avoiding unnecessary hospitalizations and cutting costs, while real-time analytics to engage patients in their care may improve physician-patient relationships. Unfortunately, AI will take much work before it becomes a part of healthcare delivery.

AI can provide powerful automated analysis that quickly connects information that might otherwise take years to process, providing key solutions to these challenges in drug development and patient monitoring. Artificial Intelligence offers one solution by rapidly processing vast amounts of data accumulated every day to deliver meaningful insights.

Deep learning combined with radiology algorithms enables physicians to quickly and accurately spot changes that would otherwise remain invisible to the naked eye. Risk prediction tools and sepsis detection may reduce unnecessary hospitalizations while saving lives.

AI can assist with early disease identification and diagnosis by scanning large datasets to search for patterns indicative of certain conditions such as pulmonary hypertension and cardiac amyloidosis, where many symptoms overlap with those seen elsewhere. AI technology can also detect abnormalities indicative of such diseases such as amyloidosis in cells.

As healthcare increasingly transitions from hospitals into homes and communities, AI can ensure patients’ needs are being met by analyzing data from medical-grade wearables. For example, EKG records can alert clinicians of atrial fibrillation – a potentially life-threatening heart condition which could result in stroke.