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Everything you need to know about AI in Healthcare

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Automate manual tasks and enhance patient experience.



What is AI in Healthcare?

The healthcare industry is constantly growing, and with it, the data used is becoming phenomenally huge and more complex. Therefore, accuracy and speed in gathering and accessing data are paramount for healthcare providers to offer prompt and effective medical care.

Machine Learning(ML) and other AI technologies, including Deep Learning (DL), Natural Language Processing (NLP), etc., have aimed to imitate human functioning and have been shown to supersede humans in specific areas such as disease diagnosis. Hence, there is no denying that AI has proven its capability to learn and perform tasks just as humans.

AI is being increasingly applied in healthcare in various ways. A few notable ones are:

  • Disease diagnosis and specific treatment planning.
  • Enhanced patient monitoring and interactions.
  • Assisting medical research.
  • Supporting administrative activities.

AI is advantageous as it can streamline tasks, improve productivity, provide guidance during complex procedures to make them simple and less time-consuming, and reduce stress and effort on doctors to focus on better patient care.

AI can bridge gaps in making healthcare accessible to developing countries with limited resources and infrastructure. Digital self-assessments, remote monitoring, and data extraction are some of its capabilities that make healthcare accessible to everyone.

Types of AI in Healthcare

Artificial Intelligence (AI) comprises many different technologies that may be used individually or in conjunction to support varied tasks and processes in the healthcare domain. The most popular and important ones are highlighted below.

Machine Learning (ML)

The most commonly used form of AI in healthcare, ML is the core technology that propels many AI applications. Of late, it has been used extensively in precision medicine to perform a customized line of treatment for a patient taking in factors such as genes, lifestyle, etc., to get specified outcomes.

Deep Learning (DL), a popular neural network and a form of ML, has been used to categorize raw medical data to drive valuable decisions in healthcare. For example, genetic research can predict if an individual is at risk of developing diseases such as cancer or heart disease.

Natural Language Processing (NLP)

Aiming to understand and interpret human language has driven AI for decades. NLP is one such technology that aims to mimic human language abilities by recognizing spoken words and understanding text to create meaningful communication or content.

In healthcare, the use of NLP involves applications such as speech-text analysis and translation that can help with the creation and classification of medical documentation or research. For example, transcribing doctor-patient interactions for better analysis and outcomes, creating diagnostic models, improving triage systems, etc., are made possible with NLP.

Physical Robots

Physical robots have been rising in many industries, and healthcare is no exception. Approved in 2000, surgical robots have helped make surgical procedures easier and faster with high adherence to precision and perfection. Minimally invasive incisions, 3D mapping of surgical sites for better visibility, and faster stitching of wounds are advantages robots can offer to reduce errors and make surgeries successful.

Robotic Process Automation (RPA)

Unlike its name, RPA does not involve physical robots but uses computer programs or bots that perform repetitive tasks. As a result, RPA helps ease administrative workflows and smoothen support systems in healthcare with better efficiency and improved patient care. Using RPA in healthcare includes appointment scheduling, updating patient health records, insurance claims processing, bill settlement, and more.

RPA can perform data extraction for system input in conjunction with image recognition technology, such as producing reports, x-rays, or digital images for doctors from patients located in remote places for correct advice and medical support.


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How is AI Used in Healthcare?

AI has already been breaking barriers in the healthcare industry. AI has been found to efficiently tackle some of the common but most significant problems in the medical industry. The use of AI in the medical industry can be extensive, commonplace, and time-saving. AI is/can be used to:

Accurately diagnose and minimize the risk of errors

Accurate diagnosis plays a critical role in healthcare. Unfortunately, a study on diagnostic errors showed that 5.08% or 12 million US adults are misdiagnosed in a year. Misdiagnosis can result in substantial medical expenses, cause physical suffering and pain, and cost lives.

Though still in the infancy stage, AI has the potential to diagnose certain diseases, including cancer. Using capabilities such as Machine Learning (ML) and Natural Language Processing (NLP), AI can quickly scour through medical histories, learn by watching humans and adapt to using complex algorithms to accurately diagnose medical conditions akin to specialized practitioners.

Aid in the development of new drugs

Finding new drugs is a continual effort in the medical field and can bring much promise to tackle constantly evolving diseases or newfound ones. The efficiency and accuracy rate at which AI can perform tasks using machine learning is helpful in the process of drug discoveries. AI-based models can simultaneously run different programs, identify correct resources, and take over every clinical trial stage to monitor, analyze, and produce reliable results in a lesser turnaround time.

Monitor patients using virtual nurses or bots

Whether inpatient monitoring at a hospital or remote monitoring at faraway locations, AI can ensure healthcare support at all times. For example, virtual nurses that use AI capabilities help monitor critical patients’ activities in intensive care units (ICUs). They can also be used to provide blood reports or help analyze images from patients in remote areas for diagnosis and advice. In addition, AI-based virtual bots facilitate 24/7 communication with patients in-between hospital visits, which may be questions regarding admission, doctor availability, help with diet schedules, and more.

Manage medical data and maintain patient records

The healthcare industry thrives on data, and losing them could be costly and hamper progress and innovation from happening. AI can intelligently process vast volumes of relevant data, classify, and store them securely for future availability. Apart from maintaining up-to-date electronic health records (EHR), AI with deep learning is also used to record doctor-patient interactions, line of treatments, etc., for future analysis.

Perform robot-assisted surgery

The use of robots in surgeries is not new in the healthcare industry. However, AI robots have been used effectively for the past couple of decades in complex or time-consuming surgeries to reduce complications, time, and effort that can be exhausting for the personnel involved.

Robots provide medical professionals with real-time data, intelligently guide a surgical procedure with a magnified and detailed view of the surgical site, and handle complex surgeries with impeccable precision.

Streamline administrative workflows

Automating administrative tasks can result in significant savings in the healthcare industry. For example, doctors and other healthcare providers can efficiently use AI technology such as voice-to-text transcriptions to take notes during procedures, consultations, ordering lab tests, and planning patient-specific treatment courses.

The use of robotic process automation (RPA) and other technologies can help gather required medical data and automate mundane and repetitive tasks, thereby reducing overload on administrative staff and enabling smoother healthcare support systems.


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Benefits of Using AI in Healthcare

AI with the capacity to learn, understand, interpret, predict, and guide holds big promise in healthcare. It can be advantageous to medical care providers to analyze, diagnose, and help patients with better focus and treatment support. Some of the huge benefits that AI can offer are as follows:

  • Supports clinical decision-making with real-time data – AI can provide real-time data instantaneously to aid in precise and beneficial decision-making. ML algorithms can identify potential risks, provide status alerts on critical or emergency patients, prevent diagnostic errors, and help foster doctor-patient relationships with satisfactory outcomes.
  • Enables smoother processes and task completion – AI has brought many innovations in the way operations and tasks are carried out. Scheduling appointments, surgeries, follow-up visits, tracking medical history, updating patient details from different departments, and reviewing and resolving faulty insurance claims, are all made non-chaotic and easier with the use of AI
  • Provides savings on cost, time, and resources – Automation with AI has helped doctors analyze and diagnose diseases in a fraction of the time it took previously. AI enhances precision medicine with customized treatments instead of generalized approaches to save effort and cost for patients and the medical fraternity. Early risk detection and prioritizing treatments enable faster medical attention in lesser turnaround time with huge savings on cost and effort.
  • Supports effective medical research costs billions of dollars, not to mention the time and effort required to invest in medical research for better disease prevention or the discovery of new drugs. At its core is the availability of relevant data that AI can help amass from various sources with ease. Providing real-time information makes it possible to share the data across multiple networks and aid in medical discoveries that can be life-changing.
  • Lowers stress on doctors for improved productivity – Practitioners face a lot of pressure from patients and work overloads. Every patient’s data has to be entered and maintained for later reference. NLP technology can eliminate the need for manual keying through live speech recordings or speech-to-text interpretations. Doctors can maintain better patient interactions and increase productivity by handling more patients with reduced workloads and stress.
  • Facilitates better healthcare access – Developing nations lose quality healthcare access either due to a lack of medical facilities or a shortage of medical care personnel. AI can help set up a digital infrastructure that can make accurate diagnoses in such populations and guide them to suitable facilities only if there is a necessity for intervention. Remote monitoring through AI has helped patients keep in touch with doctors and receive good medical care irrespective of the distance or location.

Challenges to Using AI in Healthcare

Despite multiple uses and benefits, the implementation of AI in healthcare is still laced with numerous hurdles. Some of the challenges are:

  • Lack of standard medical data – The various datasets in healthcare vary in nature and complexity. Besides, data from one organization can be significantly different from another. The lack of standard and consolidated medical data across the industry results in fewer training scenarios for AI to comprehend and learn, leaving them underequipped to handle critical tasks like humans.
  • Performance metrics vs. clinical applicability – The various metrics used to measure the performance of AI applications do not always guarantee efficiency when applied in real-time. The metrics used to test algorithms are limited and can be vastly different when clinically applied to different patients.
  • Requires supervision as it cannot replicate human thinking – While AI robots can work straight-through complex tasks, they require human inputs to perfect them. Besides being machines that only use logic, they do not exercise understanding or observe behavioral displays, which are necessary for healthcare to avoid misdiagnosis and ensure improved and satisfactory patient outcomes.
  • Inaccuracies in data can be misleading – Providing correct medical care depends on the availability of past medical histories and research data. Inconsistent data can make machines provide inaccurate data or wrong hypotheses, limiting, misleading and costly.
  • May not consider social factors – AI may not be able to consider other factors such as economic or social background when suggesting treatment recommendations for patients. For example, individuals can avoid medical treatments owing to cost, accessibility, personal preferences, and more.
  • Privacy and security risks – Healthcare data is susceptible as it involves people’s personal information. Privacy becomes an issue when such data is revealed for safekeeping by machines. Plus, AI systems are dependent on various data sources and proper networks for efficient functioning. Security hacks can leave such data in the open, cause huge losses to organizations, and put the lives of billions of people at stake, making fraud and malpractices rampant.

Automating Document Processing in Healthcare with Nanonets

Documents have an integral role in providing information to the medical industry. Nanonets is an OCR software that can ease document processing in the healthcare industry. This intelligent software leverages AI and related capabilities to automatically extract relevant information from physical forms, PDF documents, reports, scanned files, and images. It makes them accessible for optimized patient care support.

The AI-based algorithms are easy to use, can be customized, and continuously learn to adapt to changing environments in healthcare, all the while ensuring accuracy and privacy of patient’s sensitive data.

Nanonets can be advantageous in providing hassle-free documentation processing during:

  • Patient survey processing – The use of online surveys to assess patient satisfaction has become customary in healthcare. Nanonets can capture and extract meaningful information that can be useful in understanding a patient’s needs and bridge deficits in current medical care offerings.
  • Claims processing – Nanonets software can use its intelligent OCR capabilities to analyze and extract precise information from huge volumes of patient data across structured or unstructured documents. This can help insurers verify digitized health records, verify current claims, eliminate suspicious ones, and eligible process claims quickly and without errors.
  • CAHPS – Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys aim to assess patient experience instead of patient satisfaction. Done on large samples, they use standard questions and protocols to ensure the comparison of information across the healthcare industry. Nanonets software can be customized to digitize CAHPS survey results with automatic scanning, data extraction, and verification of data to ease the time-consuming process while upholding data integrity.
  • Paper-based surveys – which use a pen-and-paper approach, are used widely by many departments in healthcare to receive general or specific feedback from patients. Nanonets uses AI to make it easier to capture, process, and store these vital details and send them to relevant departments or customer service for improvising healthcare availability.
  • EHR processing – Electronic health records (EHR) contain vast data such as past medical history, diagnoses, line of treatments used, medications, lab reports, imaging reports, insurance, etc. Collating such information is easily carried out by Nanonets through its advanced OCR software that can effectively work on both structured and unstructured documents.

Automate claims processing, digitize patient survey results and enhance EHR processing with Nanonets!


The Final Note

Healthcare is one industry where millions of lives depend on the accuracy and availability of data. As a result, AI has tremendous potential to transform the way things work and simplify the lives of patients and healthcare providers, all along ensuring cost-effectiveness in terms of time, money, effort, and resources.

Technological solutions by AI have evolved to understand, learn, and predict, making complex tasks such as surgeries, diagnosis of medical conditions, monitoring patients under any circumstances, etc., more accessible and with beneficial outcomes.

While AI implementation faces different challenges due to the complexity of data, ethical reasons, security issues, and more, intelligent systems that work as humans do can plug gaps in healthcare accessibility and promise efficient healthcare availability to make a difference in people’s lives.

Nanonets online OCR & OCR API have many interesting use cases that could optimize your business performance, save costs and boost growth. Find out how Nanonets’ use cases can apply to your product.

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  • Source: https://nanonets.com/blog/ai-in-healthcare/

This Post was originally published on AI & Machine Learning

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