The Avenga Team
The AI healthcare market is expected to hit $61.59 billion by 2027. According to the study, clinical health AI applications can save up to $150 billion annually for the U.S. healthcare economy by 2026.
Artificial Intelligence is burning hot in the entire tech world today. Some see it as a cure for all ills, starting from a top security and revenue bump to medical advancement. Is it justified? Can AI be the panacea for the complex issues in the life sciences realm? Let’s have a closer look at real-life examples. Artificial Intelligence (AI) trends embraced by healthcare and pharma industries rely on streamlined connected activities within preventive, diagnostic and remedial services; drug discovery and manufacturing; predictive analytics; smart electronic health records; correlated sales; marketing; personalized behavioral modification; management processes; and many more. When paired with a non-stop pursuit for quality and security, AI can be an effective key to the whole healthcare research and development arena as it is smarter and faster, and what is even more important – cost-efficient.
A number of healthcare organizations, together with heavyweights in tech and pharmaceuticals, along with healthcare startups with a vision for innovations, are already working on the AI-powered algorithms to improve global healthcare. But the global AI adoption trends are much wider. The recently launched Alliance for Artificial Intelligence in Healthcare (AAIH) is a global advocacy organization created with the purpose to enable the development of artificial intelligence in healthcare, providing a unified industry approach to establishing responsible and ethical standards for the implementation of AI in healthcare. It unites technology providers, pharmaceutical companies and research organizations to promote research collaborations, enable advancement and ethical use of AI in healthcare to improve people’s lives through innovations and application of artificial intelligence.
So how exactly does AI work for the health industry?
It’s all about data. Again.
AI is gaining its position as an essential tool for collecting, sorting, storing, re-formatting and tracing data in order to provide faster, more consistent access. The whole spectrum of vital data like tests analysis, X-Rays, CT scans, data entry, and other mundane tasks can all be done with lightning speed and a lot more accurately. When applied skillfully, AI has every capability of refining change management and making processes self-adapting and self-optimized.
K Health, a HIPAA-compliant AI healthcare app, collects anonymous information from users about their medical background and chronic conditions. By analyzing patients’ data in the database, the app can provide insights and tips on how to better manage someone’s medical condition. Any user can enter symptoms and ailments on the app to see a list of potential diagnoses. Based on the information entered, users are shown the option to connect with appropriate specialists in their local area.
AI technologies also provide drug researchers with a more focused analysis of greater unstructured data sets, patterns identification, and consequently generate advanced algorithms to accurately optimize human decision-making. The computational power AI offers aids drug researchers with hypotheses testing and simultaneous generation, and testing at an exceptional speed which results in a faster and cheaper discovery process with risk mitigation and real-life applicable results.
Augusta, a data preprocessing and analytics application launched by BioSymetrics, analyzes and integrates various biomedical and health data with existing business processes, with the help of machine learning. This is also applicable in precision medicine, drug discovery, and health data applications.
Using data to train and sustain process change, and discovering new patterns of opportunity are the areas where AI perfectly matches the critical needs of the healthcare industry.
AI is recognized to be so much better at predictions of treatment outcomes. By utilizing great numbers of normalized data, detection of real-world data, collecting and processing big data, text mining of clinical literature and machine learning algorithms, it is possible to build an intuitive predictive model which enables clinicians to better understand data and its outcomes without the normal bulky research and extra time, which could be a matter of survival for someone. Predictive models can help launch better and more effective drugs based on machine-generated predictions compiling the clinical, social and personal conditions as well as the doctor deciding on the next steps of treatment.
According to the University of Alberta science team, a machine learning algorithm is able to identify, with 78% accuracy, schizophrenia in patients based on brain scans of the superior temporal cortex. The algorithm also demonstrates the accuracy rate of 82% in predicting the response to a specific antipsychotic medication of the patient.
The UK National Health Service uses Google’s DeepMind to detect certain health risks through data collected via a mobile app. Google’s neural networks can diagnose 50 eye diseases, through the IBM-created Watson app that is for health and early disease detection.
Apple enabled medical researchers to track Parkinson’s Disease using the iPhone X for vision tests, a hearing test featured in its AirPods, and a speech recognition tool to detect speech impediments associated with stroke.
Apps, like Babylon in the UK, use AI to give medical consultations based on personal medical history and common medical knowledge. Users report their symptoms to the app, which uses speech recognition to compare against a database of illnesses. Babylon then offers a recommended action, taking into account the user’s medical history.
Boston Children’s Hospital developed an app for Amazon’s Alexa that gives basic health information and advice to parents of sick children. The app answers questions about medications and whether symptoms require a doctor’s visit.
Personalized drug plans are designed to improve treatment after analyzing all the key data.
The US National Institute of Health created the AiCure app to monitor the medication intake by a patient. The smartphone’s webcam is partnered with AI to autonomously confirm that patients are taking their prescriptions and helps them manage their condition.
The now emerging AI-powered health decision support can potentially supplement electronic prescribing systems in rule-based decisions offering much wider analytics thus eliminating clinical errors, including drug-allergy interactions, polypharmacy side effects, therapeutic duplications, etc.
The Stanford University computer science team built a machine learning application that detects and analyzes potential side effects of drug combinations based on how each medication effects certains proteins in the human body.
The BenevolentAI platform pairs drug treatments with the patients who need them. It is being used to develop therapies for incurable diseases such as motor neuron disease, Parkinson’s disease, glioblastoma and sarcopenia.
Pharma today faces huge challenges in terms of new drug development and perfecting the already approved ones. The advancement of precision medicine and the costs of developing new medications are putting great pressure on pharma market players.
The experts note that the data pool of pharma organizations consists of 90% for dark and unstructured data, that is pharmaceutical manufacturers in many cases have to make vital million-dollar decisions with insufficient information.
That is exactly where great hopes are put into machine-powered intelligence to make drug research and development effective, and less expensive. Starting from data accumulation, pattern recognition, accelerated processing, on up to deeper biological insights driven by machine learning, efficient patient recruitment, marketing feedback and clinical trials efficacy, AI can be applied at every stage in the pharma manufacturing business.
AccutarBio found that most pre-clinical research can be replaced by different forms of trained AI algorithms, and the time spent on research can also be reduced from years to months, days, or even hours.
The heavyweights in the pharma industry are already leveraging modern computing resources, machine learning and deep learning algorithms to learn and predict molecular behavior without the bulky multiple tests for creating new super drugs, which saves an enormous amount of time and money.
Merck KGaA developed two drugs with the help of computer vision software. The technology analyzes cell and tissue images, and when coupled with other AI systems, it can draw insight from public databases of genetic and chemical information.
Putting sentiments aside, the majority of experts agree that AI is displaying real-life results in maintaining better quality and faster service in the complex realm of life sciences. Artificial Intelligence and machine learning tools are on the way to augmenting human efforts in healthcare and pharma industries transformation towards efficacy, speed and affordability and making them work for every single patient.
Tech companies like Avenga enter healthcare sector applying AI for virtually every area. Our delivered solutions powered by machine learning, deep learning, NLP (natural language processing), NLU (natural language understanding), NLG (natural language generation) and computer vision demonstrate immense potential for our customers and partners in aiding clinical decisions, accelerating clinical trials, assessing clinical image analysis, automating administrative tasks, enhancing customer experience and many more.