All the titans of the pharma market, including AstraZeneca, Novartis, Johnson & Johnson, Merck, GlaxoSmithKline, Roche and Pfizer, have reported either collaborating with or acquiring AI to leverage its promise for healthcare.
But is artificial intelligence really a magical solution to the complex issues that life sciences have been dealing with? Let’s examine things much more closely.
Coupled with the incessant search for both quality and security, AI can play a critical role in healthcare and pharma because it’s smarter, easier, and faster, and even more essentially, cost-efficient.
AI is capable of intervening in, and even transforming, areas in the healthcare industry including:
Key stakeholders in healthcare, including global tech giants and leading pharmaceutical firms, have been working for some time now on potentially revolutionary AI-driven algorithms to improve healthcare around the world. Further, the Alliance for AI in Healthcare or AAIH was recently launched to fast-track the development and adoption of AI technologies in pharma and healthcare. This is a worldwide advocacy group that seeks to offer a unified approach for key players in healthcare, to create cross-cutting standards for the application of AI in the industry.
AI for Better Data Processing and Management
Getting into the specifics, have you ever wondered what exact mechanisms will these stakeholders use to bring artificial intelligence onboard in the healthcare industry? As you may have guessed, it’s mostly about data. AI will be used to enhance both data processing and management. As it has been used in other sectors, AI can act as an important tool for the gathering, collection, sorting, formatting, storing and tracing of data in a bid to offer quicker and more consistent access whenever required. Vital data-dependent operations such as X-rays and CT scans can now be done much faster and more accurately.
One example of how AI can be applied to improve both processes and outcomes in the health industry is a HIPAA-compliant app known as K Health. This app has been used to accurately gather information anonymously from users on areas such as their medical background and any chronic diseases and conditions they may be suffering from.
By analyzing this patient data, K Health can offer healthcare professionals better insight on how to manage a particular patient’s medical situation. Anyone using the app can feed their symptoms into it to receive a possible diagnosis and even get linked to a specialist in their locality.
Still, with the potential of AI technologies to transform the pharma industry, drug researchers will have much more focused study and analysis of bigger unstructured data sets. It’ll be easier to identify patterns faster. Even more importantly, they’ll be in a better position to create more advanced algorithms that can more precisely optimize their decision-making processes. Such benefits arising from the computational power of AI consequently not only enhance risk mitigation and disease prevention, but also have real-life application in treatment.
An example of a company that has been able to implement AI in many of the areas we have just discussed is Arpeggio Biosciences. This firm, which applies machine learning in its processes, employs AI in the aggregation and synthesis of information to enhance their data analytics methods.
Digital Consultation, Diagnosis and Treatment Outcome Forecasting Using AI
If there’s one area where AI has proven to perfectly match the very healthcare needs that it has been sought for, it has to be in the use of data sets to both train and sustain change in processes. This also includes the discovery of new patterns of opportunity to enhance the various processes.
As far as predicting treatment results is concerned, artificial intelligence has also proven to be much better than traditional methods. The technology is capable of utilizing far greater amounts of normalized data than humans can. Besides, AI also makes it easier to detect, collect, and process big real-world data. It has made text mining of key clinical literature much more flawless. It has enabled the generation of smart machine learning algorithms.
All these capabilities have made it possible for clinicians to come up with intuitive predictive models that enable them to better understand data sets and treatment outcomes without having to go through the usual time-consuming and energy-draining research.
For those practitioners who’ve handled critical medical cases using both AI and traditional methods of research, diagnostics, and treatment, they’ll clearly tell you how this easing of processes can be a matter of life and death for some patients: for instance, critically injured accident victims.
In the UK, the National Health Service has reported using Google’s DeepMind to help its clinicians in the detection of particular health risks by using a mobile app to gather patient data. According to statistics provided by Google, its neural networks are able to diagnose up to 50 eye ailments via the IBM-built Watson app. Apple has also done the same using an iPhone X app that can track Parkinson’s Disease.
In the US, Bayer, Merck and Co were recently granted the Breakthrough Device Designation by the FDA for AI software that’s geared toward supporting clinical decision-making for CTEPH patients.
Chinese tech giant Tencent Holdings is currently collaborating with Britain-based Medopad to apply artificial intelligence in the remote monitoring of progress in people with Parkinson’s disease. This can tremendously lower the time it takes to do a proper motor function assessment, from more than half an hour to three minutes or below.
It’s clear that tech providers like the IT Services Competence Platform have a huge role to play in the healthcare sector by employing artificial intelligence. Our machine learning-powered solutions incorporate deep learning, natural language understanding (NLU), natural language processing (NLP), natural language generation (NLG) and computer vision to demonstrate the immense potential that AI offers in helping in clinical decisions, to quicken the process of clinical trials, in the assessment of clinical image analysis, the automation of managerial tasks, and to enhance our customers’ experience with us.
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