Artificial Intelligence Applications in Healthcare
Artificial intelligence is transforming and revolutionizing the modern healthcare system. Its power lies in its ability to reason out the patient data and provide life-saving medical insights better than human doctors in certain applications.
Artificial intelligence (AI) has made inroads in almost every sphere of our lives. With its promise to bring the additional economic value of over $13 trillion by 2030, many industry leaders are looking for ways to invest and extract revenue from the innovative technology. Healthcare sees the most applications of AI in the near future.
Artificial intelligence refers to software models and programs that can autonomously emulate human thinking. These models utilize learning and perception capabilities based on the data from the outside world to create insights or perform actions that lead to a solution to the problem.
The computing power of modern hardware and the capabilities of cutting-edge AI applications combined allow us to reduce the human error factor in the decision-making processes. In this context, AI coupled with other emerging technologies such as blockchain and & IoT becomes a powerful tool in the hands of healthcare professionals. It creates more efficient ways of analyzing, interpreting, and understanding complex medical and healthcare data.
Artificial intelligence capabilities in healthcare
Artificial intelligence solutions provide an advanced knowledge depository for the healthcare system, learning from historical databases along with real-time inputs from practitioners, doctors, and researchers. With the steady flow of new data and continuous update of AI algorithms, doctors have unlimited resources accessible to improve diagnosis and the course of treatment capabilities.
As the healthcare data in the forms of medical notes, electronic recordings from medical devices, laboratory images, and physical examinations are updated, the AI algorithms learn from newly updated data and improve the accuracy and relevance of their outputs. Several studies have shown the benefits of AI in the R&D process of developing new drugs as well.
The various types of AI that help automate these processes are machine learning & deep learning, natural language processing, robotics, robotic process automation, cognition-based automation, and rule-based expert systems.
Machine learning and deep learning
Machine learning makes use of training data to build models of real-world scenarios. Models “learn” from data and produce results without being explicitly programmed to do so. Machine learning is the most implementation of AI technology and the majority of businesses around the world have integrated machine learning into their operations.
One of the machine learning methods that became popular with the growing number of publicly available datasets is the neural network. Neural networks consist of interconnected layers of artificial neurons capable of modeling complex relations between inputs and outputs.
With the advancement in computing hardware and mathematical research, scientists recently enhanced neural networks with many interconnecting layers to capture even more complex relations in data and their implications to outputs. This relatively new concept in AI implementation has been identified as deep learning.
Given the symptoms, genetic makeup, and other medical data as input, deep neural networks are able to precisely predict a person’s likelihood of developing a particular disease. These types of AI models are used in radionics to diagnose patients based on medical images.
Deep learning algorithms categorize data and extract the clinically relevant features from radiographic images that cannot be so easily perceived by the human eye. MRI and other medical imaging systems used for early cancer detection employ deep learning algorithms to provide a more precise diagnosis, even in unusual cases.
Natural language processing
Natural Language Processing (NLP) algorithms use machine learning techniques to process unstructured data from text, audio, and video sources. Healthcare data and information are accumulated from scientific articles, technical and administrative reports, and electronic health records. Natural language processing technology helps convert unstructured data from these sources to structured data that can be utilized by doctors for diagnosis and treatment.
NLP can also produce insightful data from the patient-doctor conversations, doctor’s voice notes, and other speech-based situations such as meetings and conferences. Most popular NLP techniques for extracting information from unstructured healthcare data are:
- Named Entity Recognition (NER) extracts the entities from unstructured texts and classifies them in predefined categories such as people, locations, organization, dates, etc.
- Sentiment Analysis and Emotion Detection is the most widely used technique in NLP to recognize opinions and feedback from the text. The basic output of the sentiment analysis is positive, neutral, or negative. Nevertheless, depending on the given application, the output can also be a numeric score for a variety of different categories.
- Text Summarization is an NLP technique used to summarize large blocks of texts. It uses either extraction or abstraction methods to capture essential information in medical documents.
- Information Retrieval helps find relevant documents stored within large databases, such as electronic health records or the scientific literature. Information retrieval is very valuable for healthcare due to the large volumes of information accumulated from various sources.
- Question Answering (QA) allows digital systems to receive questions in natural language, automatically process them, and instantly provide answers. The low availability of information about diseases, medications, and medical procedures, especially in developing countries, makes QA increasingly important to healthcare professionals.
Advancements in collaborative robotics and AI-based control of robots has allowed for widespread application of robots in healthcare with prime areas of focus being to provide assistance in performing tasks for medical staff, help patients in recovery, and to accommodate surgeons in performing medical procedures.
Assistant robots for medical staff increase efficiency by allowing them to focus on more pressing issues and also make medical procedures safer and cost-effective. Assistant robots help with room and operating site disinfection. They work in laboratories to obtain, transport, analyze, and store test specimens. In radiopharmaceuticals, assistant robots prepare the radioactive samples to reduce radiation exposure. In hospital facilities, they are used for typical daily logistics such as to carry bed linens and meals.
Assistant robots for patients support them during the recovery and treatment stage. These robots are capable of monitoring a patient’s vital conditions and alerting the medical staff in critical situations. They input the patient’s information automatically into their electronic health records and allow nurses to monitor multiple patients at the same time. Assistant robots support patients during physical rehabilitation by helping them perform recommended physical activities. “Social robots” assist elderly people by keeping them company after medical treatment. With the help of advanced AI software, these robots can respond to human speech, recognize emotion, and keep patients entertained.
Surgical robots assist surgeons in the operating room. Robots can make more precise incisions than human surgeons, increasing the efficiency and success of minimally invasive surgeries. Modern surgical robots feature high-definition 3D cameras that are superior to the human eye and tiny wristed instruments that can bend, extend, and rotate surpassing the capabilities of the human hand.
Robotic process automation
Robotic process automation (RPA) systems automate repetitive tasks, taking full control over processes that do not require decision-making or human intervention such as prior authorization, patient records update, and billing.
Jobs that involve dealing with large amounts of data and require decision-making can be handled by cognitive automation technologies as they can mimic human behavior in these situations. Cognitive automation systems offer such capabilities as recognition of patterns, images, text, handwriting, natural language processing, semantic computing, and probabilistic inference. They are used in healthcare to perform most of the mundane administrative tasks, leaving the human workforce as the supervisor to oversee these processes.
Rule-based expert systems
Rule-based expert systems provide support for diagnosis or treatment planning at the level of proficiency that an expert doctor would provide. The majority of these systems make use of the set of rules previously modeled by medical experts. Nevertheless, more recent rule-based expert systems may also include probabilistic relations, such as between symptoms and diseases, augmented by causality and temporal relationships as well.
Artificial intelligence applications in healthcare
Artificial intelligence has a multitude of uses in healthcare. The most successful applications of AI in healthcare are the areas of disease diagnosis, development of treatment protocol, new drug development, and personalized medicine.
Disease diagnosis applications
Disease diagnosis is a challenging and time-consuming process in healthcare. It takes doctors years of medical training to be able to diagnose diseases accurately. While the demand for diagnosis experts keeps rising globally, many healthcare facilities are currently understaffed. This situation puts available doctors under severe strain and often delays life-saving patient diagnostics. Due to these reasons, we are witnessing a growing adoption of AI systems for disease diagnosis.
Artificial intelligence uses CT scans, electrocardiograms (ECG), cardiac MRI images, skin images, retinal scans, and X-Ray scans to detect cancer, stroke, diabetes, and other diseases. AI algorithms make use of large volumes of high-quality healthcare data to classify or predict diseases with comparable or even better accuracy than human experts.
Same as in disease diagnosis, AI is becoming invaluable for the selection of a proper treatment plan. As each patient may respond to a prescribed treatment differently, personalized treatments greatly help patients during the recovery phase.
Artificial intelligence automates the treatment selection process by making use of genetic and medical data of other patients with similar symptoms to predict the probable response to a prescribed treatment. Although most of the related research is still in the outset, the first batch of real-world tests have had promising results.
Drug discovery applications
Placing a new drug in the market is a complicated process that can last up to 10 years and cost businesses over $1.3 billion in expenses. The new drugs get stuck between initial trials and regulatory approval. As a result, only 1 out of 10 newly developed drugs ever reach the mass market despite the meticulous drug development R&D process. This state of the pharmaceutical industry is influencing experts to consider new innovative ways to manage workflow.
Strategies involving AI to assist in the drug development decision-making process are predicted to revolutionize the industry and create an additional market value of $40 billion. Artificial intelligence can optimize innovation, improve the efficiency of research and testing, and provide new tools for doctors, patients, insurers, and regulators to meet market demands.
Every stage of the pharmaceutical R&D process can be improved by AI and lead to increased efficiency:
- Literature survey. Researchers around the world publish their findings daily in the form of research papers, patents, and other types of gray literature. It is just not possible to collate the information from all of the published documents. Artificial intelligence algorithms can analyze the available literature and extract valuable insights to help researchers make informed decisions early on in the R&D process.
- Identification of principal targets for intervention. Understanding the biological origin of a disease and its resistance mechanism is the second stage in the development of a new drug. It is essential to identify principal target proteins for disease treatment. Despite the availability of data from different sources, the discovery of viable target pathways remains a great challenge for researchers. This process requires the integration and analysis of all the data to find a relevant pattern which is a perfect task for machine learning algorithms trained for pattern matching. These algorithms analyze the available data and automatically identify the target proteins for intervention.
- Discovery of possible drug candidates. After the identification of targets, researchers must find the compound that will interact with selected target molecules in the desired way. This process includes scanning millions of candidate compounds and studying their effects and side-effects on the target. Machine learning algorithms can help researchers narrow down the selection to the best drug candidates, as they are able to rapidly synthesize the information about the so-called structure fingerprints and molecular descriptors to predict the suitability of a candidate molecule.
- Clinical trials. The success of a clinical trial depends on the accurate selection of suitable candidates. The selection of wrong candidates can prolong the clinical trials and increase the cost of the drug discovery process. Artificial intelligence can help the researchers identify suitable candidates for clinical trials. Machine learning makes use of data to ensure the proper distribution for groups of participants. At this stage of a new drug development, AI algorithms monitor & predict trial cost, enrollment, and quality. They can also serve as an early warning system for a clinical trial that is not producing conclusive and/or expected results.
- Selection of biomarkers for disease diagnosis. It is possible to identify the disease either by genome sequencing or by utilizing the biological markers. Genome sequencing is a method that involves expensive lab equipment as well as expert knowledge. On the other hand, the biomarker approach is more economical and reliable. At the same time, their discovery is challenging and involves a lot of manual work. Artificial intelligence algorithms help automate the identification of suitable candidate biomarkers, which in turn, increase the prospects of success and decrease the expenses.
Personalized medicine applications
Another use case of AI in healthcare is precision medicine, a drug or treatment designed especially for an individual or a small group of people. AI systems are programmed to predict which treatment protocol will be the most successful given the patient’s physical and genetic traits and the treatment context.
The machine learning model for precision medicine learns from a large dataset that contains information such as patients’ medical records and genetic characteristics as input and the outcome of the specific treatment as the output. The trained model can precisely determine the type of treatment that patients should receive, given their medical records and genetic makeup. As more data about the patient’s treatment becomes available, the AI models become more and more efficient performing these tasks.
Examples of artificial intelligence in healthcare
The powerful capabilities of AI solutions find a multitude of applications in the Healthcare industry. Let’s take a look at some of the projects that have already taken off the ground.
Aidoc: AI for radiology
Aidoc developed a clinical decision support system that utilizes artificial intelligence algorithms to detect the haemorrhages on Computed Tomography (CT) scans. Aidoc uses deep learning to analyze the CT images and helps radiologists triage patients with potential cases of acute intracranial hemorrhage (ICH). By detecting internal bleeding faster and more accurately, doctors can diagnose and treat brain hemorrhages much more effectively.
iCAD: AI-powered breast health solutions
iCAD‘s Breast Health Solutions use deep learning technology to deliver software solutions for breast tomosynthesis, breast density, and 2D mammography. It analyzes tomosynthesis images and accurately detects malignant soft-tissue densities and calcifications. These innovative solutions offer clinicians the right tools for disease detection and analysis and enhances the treatment process and cure.
AIRA: AI for retina analyzing and disease diagnosis tool
AIRA, a solution developed by PixelPlex, uses artificial intelligence software to diagnose retinal pathologies from retinal fundus pictures. Machine learning algorithms are capable of identifying and providing medical staff with information required to determine the presence of pathology. AIRA is also used to create a digital database of retinal pathological cases, thus providing novice ophthalmologists the necessary guidance and support.
Arterys: liver/lung cancer diagnosis and MRI heart interpretation
The Arterys project leverages AI and deep learning to create diagnosis tools for heart, liver, and lung diseases. Its Viewer AI solution effectively measures and tracks liver lesions and creates visualizations indicating the pathology. It also provides longitudinal tracking and quicker volumetric segmentation. Lung AI provides tracing of lung nodules and clinical management of lung cancer in patients. Cardio AI allows automated analysis of cardiac magnetic resonance (MR) images and quickly determines treatment for tracking of heart problems.
Moorfields eye hospital: early detection of signs of eye disease
Moorfields Eye Hospital in London, in conjunction with Google DeepMind Health, is using AI to offer faster and more accurate early diagnosis of eye disease and thus prevent total loss of vision. Using patient data from 15000 individuals, the London-based hospital has trained the AI algorithm to identify signs of eye disease from optical coherence tomography (OCT).
Novasignal: lucid robotic system for brain health
By combining artificial intelligence, robotics, and automated cerebral ultrasound, the Lucid Robotic System is revolutionizing how brain health data is harnessed and utilized. Its branded robotic ultrasound reduces variability in measurement. The system employs artificial intelligence to promptly locate main blood vessels and assess the blood flow velocity.
Icometrix: MRI brain interpretation
Icometrix, a medical tech company, uses deep learning algorithms to help doctors and radiologists interpret brain MRI and CT scans. The platform allows clinicians to apply AI to help patients with traumatic brain injuries. Icobrain TBI platform makes it possible to detect a traumatic brain injury (TBI) early on so the treatment could start imminently.
Viz.ai: CT stroke diagnosis
Viz.ai is using AI and deep learning to help doctors and cardiologists diagnose stroke faster and more accurately. Their product, Viz LVO, uses artificial intelligence to immediately detect suspected major vessel occlusion strokes on CT angiogram imaging. It enables doctors to recommend more advanced treatment options like thrombectomy and refer serious cases to specialized stroke centers.
Google Health and DeepMind build products that support care teams and improve patient outcomes. Google Health leverages the potential of AI to help in cancer diagnosis, predict patient outcomes, and avert blindness. It has recently come up with an AI-based solution for identifying breast cancer. The algorithm outperformed all human radiologists on average by 11.5%.
IBM Watson Health
IBM has a dedicated health branch, Watson Health, that employs AI to help stakeholders within the healthcare sector from payers to providers. With the power of cognitive computing, Watson Health has aided several renowned organizations like Mayo Clinic with its breast cancer clinical trial and Biorasi to bring drugs to the market faster while slashing costs by over 50%.
Amplion: biomarker strategic planning for pharma
Amplion uses AI to synthesize biomedical knowledge and biomarker expertise to guide biomarker strategic planning. It provides tools for researchers to ensure an adequate mix of biomarkers, establish differentiation, recruit the right patients, and identify the best companion diagnostic opportunities.
IRIS.AI: research discovery
IRIS.AI uses Natural Language Processing to go through a massive collection of research papers and patents. They establish and find the similarity of the document “fingerprints” based on a combination of keyword extraction, word embeddings, neural topic modeling, and other natural language understanding techniques. IRIS.AI finds the right documents, extracts its valuable data, and identifies precise information for researchers.
Risks and challenges
AI adoption in healthcare is still in its infancy. Many companies creating new or upgrading current solutions find the benefits of AI systems very encouraging. Nevertheless, every novel technology in healthcare bears certain risks. The most prominent challenges that AI is facing in its adoption are related to the AI-caused errors, availability of quality data for model training, and patient privacy.
Errors and injuries
Artificial intelligence algorithms trained using low quality, incomplete or biased data will produce erroneous results. The biggest challenge with AI caused mistakes in healthcare is accountability, who is responsible for the error.
A recent study has shown that, despite its potential to revolutionize modern medicine, AI-based medical imaging has serious shortcomings. AI algorithms used for medical image reconstruction result in unwanted alterations in data in the final images. These effects were not typically present in non-AI based imaging techniques.
Even though medical errors occur in all healthcare systems, AI-caused errors are different for two reasons. First, patients and healthcare providers will react differently to injuries and suffering resulting from software than humans, seeking accountability. Second, the mistakes made by the AI system will have a large scale impact due to their widespread use, while human errors usually are localized affecting a limited number of patients.
Quality of data
AI algorithms require a large volume of data for training. The available data often must be obtained from multiple different systems and sources in various formats. The collection process is expensive, increases the risk of having errors, and decreases the comprehensiveness of the dataset.
Healthcare data may often be of low quality, subjective, and inaccurate. For example, electronic medical records are unstructured, patients may give false information, or data is incomplete due to privacy reasons.
Machine learning algorithms used in healthcare systems are prone to algorithmic bias as well, sometimes predicting the higher likelihood of a disease based on gender or race when it may not be causal.
For example, if the training data comes from academic medical centers, the machine learning algorithms will “know” less about, and therefore may establish a wrong diagnosis for patients that do not have access to academic medical centers. Similarly, when using speech-recognition AI to obtain the patient visit notes, algorithms may perform worse when patients belong to a gender or race that is underrepresented in the training data.
Another challenge associated with AI in healthcare is related to privacy. The collection of data for training of AI algorithms can violate patient privacy. Already law-suits are being initiated due to the data-sharing between large healthcare providers and AI-companies. The privacy problem associated with AI algorithms goes further as these can predict a patient’s private information without it explicitly given in the data.
Due to the seriousness of the privacy issue, some solutions have emerged, namely, to assemble high-quality datasets while protecting patient privacy, governments should be involved in providing the infrastructure. This infrastructure could range from setting standards for electronic health records to directly providing technical support for high-quality data-gathering efforts in health systems. The second approach to the solution is a direct investment in the creation of high-quality datasets. Two initiatives, All of Us and BioBank, have already developed plans to collect comprehensive healthcare data while facilitating the trust and participation of the patients.
The future of AI in healthcare
As the paradigm shifts universally to conveying health from illness management to wellness maintenance, AI will play a very significant role in the personalization of healthcare. The availability of high-quality and comprehensive data will be an essential factor for the future success of AI in healthcare.
IoT-enabled patient monitoring, asset tracking, and inventory management systems will provide invaluable data as input to AI algorithms to improve the health of patients, working conditions of health workers, and healthcare system operational efficiency.
As the AI algorithms continue to get better at their tasks, the robots used in healthcare will become more prominent. Robots will assist and collaborate with humans to handle time- and energy-consuming tasks and help prevent operational errors.
Patient privacy concerns are receding with the development of large scale AI systems for healthcare. In the future, blockchain technology will be combined with AI to mitigate privacy concerns and establish a more secure and transparent healthcare system.
Artificial intelligence will enable doctors to access a large number of diagnostic resources on web databases and find the desired research study or clinical report faster and more efficiently.
Despite common fears that AI may eventually replace or reduce the need for human doctors, recent studies have shown that AI will more likely benefit and enhance clinical diagnostics and decision-making processes than reduce the need for physicians. The synergy between artificial intelligence and human intelligence promises a bright future for the healthcare system.
Artificial intelligence is becoming an integral tool in healthcare. The most promising applications of AI are the ones where AI can either make decisions more efficiently than humans or where it can simplify complex medical data to allow humans to make more informed decisions. For example, AI benefits the doctors by providing a quantitative and qualitative assessment of patients that have multiple symptoms correlating with various genetic and physical conditions. In such cases, AI improves the accuracy of early detection, diagnosis, treatment, and outcome prediction.
As machine learning, natural language processing, deep learning, and robotics technologies progress many new applications of AI in healthcare will emerge. It is a crucial time for healthcare stakeholders to start incorporating AI in their work processes.
PixelPlex, an AI development company, can help healthcare providers to incorporate AI into their businesses. PixelPlex utilizes predictive algorithms and analytics solutions to provide tools for smart medical diagnosis. We recommend you to check out our AI and machine learning projects and get in touch with us to discuss your idea/s.