MAY 22, 2022
Artificial Intelligence (AI) in Health Care
NOV 03, 2021
Out of all the industries that stand to benefit from artificial intelligence (AI), health care is arguably the most universally crucial and relevant.
The recent accelerated COVID-10 vaccine development efforts are just a few examples of how AI-driven medical innovations can be critical to everyone’s well-being.
That said, drug discovery is just one of the many health care/medical fields and specialties that AI has transformed.
The market size for health care AI and cognitive computing reached $6.7 billion in 2021, at a compound annual growth rate of 40 percent, compared to $811 million back in 2015, according to a recent Frost & Sullivan report.
Some areas of heightened growth include AI applications in medical imaging diagnosis, AI-based solutions for optimizing hospital workflows and enhancing care delivery as well as use cases for reducing patient treatment times, complexity, and costs.
See more: Artificial Intelligence Market
The expedited response of vaccine researchers to the pandemic was aided by AI.
AI algorithms have helped to break new ground in accelerating the discovery of new molecular combinations, tracing toxicity potentials, identifying active mechanisms, and a myriad of other drug discovery applications.
Interestingly, in Moderna’s case, AI helped to both speed up coronavirus vaccine development and automate other key systems and processes in the company.
The timely, accurate assessment of a patient’s condition is critical for effective treatment and recovery.
To this end, AI has brought about major advances in case triaging or determining when cases are urgent or non-urgent.
For example, radiologists and cardiologists are using AI-based solutions to automatically review images and scans. This enables them to quickly identify key insights and prioritize emergency cases.
AI-assisted diagnostic imaging is widely considered one of the most promising clinical applications for AI in health care.
In a crisis such as the pandemic, heightened urgency makes the speed of drug development a high-priority concern.
Drug research and discovery budgets under normal circumstances are heavily allocated to experimentation-related activities and processes.
Through the use of AI, such as convolutional neural networks, predictions can be automated regarding complex processes, including the binding of molecules to proteins. Because AI-enabled solutions can analyze hints and signals from vast quantities of experimental measurements faster than teams of researchers could on their own, safe and effective drug candidates can be identified in less time and with significant cost reductions.
Other innovations improve long-term efficacy as a cost-reduction measure.
For example, solutions like the digital pill combine personalized, AI-based tools with standard drug prescriptions for better patient response to drugs, increased adherence, and improved management of chronic medication intake.
A plethora of AI-powered apps for iOS and Android are available for managing and enhancing users’ psychological well-being.
Though highly popular, these solutions have mostly been of consumer-grade quality and limited to mobile device use. Recently, companies like Kernel have emerged with medical-grade software/hardware solutions that use AI/machine learning (ML) to quantify and understand the human brain for more accurate mental health assessments and treatment.
Google’s DeepMind Health has also developed a technology that merges ML with system neuroscience to build neural networks that mimic the human brain. Partnering with clinicians, researchers, and patients, Google aims to apply its AI prowess in solving real-world health care problems.
As cancer is the leading cause of death worldwide, a myriad of oncology-related AI solutions have taken on the multifaceted, complex challenge of diagnosing and treating the disease.
Companies like PathAI develop ML-based solutions for both helping pathologists make more accurate diagnoses and developing effective methods for highly individualized cancer treatments.
AI-powered breast cancer screening is another active health care space that combines advanced computer imaging with ML-powered detection of suspicious features as well as predictions regarding likelihood of malignancy. Dutch spin-off Screenpoint Medical, for instance, says its AI systems for assessing mammograms can perform better diagnoses than the average mammography radiologist.
AI health care solution providers are also taking on cancer at the molecular level. For example, German biotechnology firm Evotec recently partnered up with AI drug discovery firm Exscientia to apply AI techniques to small molecule drug discovery. The partners have announced the start of a phase 1 clinical trial for a novel anti-cancer molecule.