In an era where digital is no longer an option and disruptions occur more often than ever, medical science is shifting towards artificial intelligence (AI) and machine learning (ML) to prepare for the next generation. Amidst growing patient experience demands, telemedicine disruptions, and the massive influx of data from different health-tech wearables, the medical sciences industry – in particular, care delivery – is in dire need of change for:
- Advanced analytics of huge data volumes
- Accelerated assistance on the frontlines
- Integrated data collection, classification, and validation
- Fast and efficient drug discovery and distribution
- Population analysis for disease impact and management
In dealing with repetitive tasks and the COVID-ushered stigma to ‘predict and prevent,’ the medical sciences industry is looking at AI/ML to explore new and effective ways of care delivery. Apart from liberating care providers to focus on cognitive tasks and patient care, this technology is poised to take medical sciences to the next level.
Companies like IBM are already leveraging AI/ML to power medical sciences with Watson for Health. AI/ML can help in the development of algorithms to explain patterns and connections between unassociated data points. What’s more, it can help in assessing wounds for infection through the synthesis of aesthetic observations, analyzing complicated scans and images, interacting with patients, designing therapies, and even cut both the cost and time associated with drug discovery – a process that arguably takes 12 years to fruition. To put that into perspective, this is a glimpse of how AI/ML can be employed across the value chain:
AI/ML can not only automate repetitive tasks but also enable a comprehensive approach to disease management, chatbot-enabled patient care, prescription auditing, claims processing, and long-term treatment compliance programs.
Text mining combined with AI/ML provides an increased value to clinical research with quick identification of statistical patterns. It can enable faster, less expensive, and more efficient pharmaceutical research and drug discovery.
From assessing wounds for infection by synthesizing aesthetic observations and syndromic surveillance to electronic health records (EHRs) analysis and data-driven preventive care, AI/ML can unlock multiple opportunities in diagnosis.
AI/ML supports naturalistic simulations for training and NLP-infused programs for augmenting the quality of learning better than what was possible through simple computer-driven algorithms.
From prescreening to medical imaging and patient triage – AI/ML can be deployed with IoMT, deep learning, or analytics solutions to enable timely decisions both in clinical and administrative scenarios.
With an accuracy of 90%-95%, AI/ML-enabled systems can transform the roles and responsibilities of radiographers across modalities.