Technology has always been the foundation of the healthcare industry. The need for digitalization in this industry is more than ever today. AI and ML can help accelerate drug discovery and predict early diagnosis accurately for prompt and effective treatment of a disease.
Revolutionizing Healthcare Domain with ML and AI
Key Applications of ML and AI in Healthcare
- AI-assisted Radiology and Pathology – As we have abundant electronically stored medical imaging data, and DL (Deep Learning) algorithms can be fed with this dataset, to detect and discover patterns and anomalies. ML algorithms can interpret the imaging data much like a highly trained radiologist could identifying suspicious spots on the skin, lesions, tumors, and brain bleeds. The usage of AI/ML tools/platforms for assisting radiologists is, therefore primed to expand exponentially.
- ML and Data Science for Actionable Insights – We have exabytes-sized medical data, which are being digitized at various healthcare institutions (public hospitals, nursing homes, doctors’ clinics, pathology labs, etc.). Unfortunately, this data is often unstructured and difficult to interpret. Unlike standard transactional type business data, patient data is not particularly amenable to simple statistical modeling and analytics.
Robust and agile AI-enabled platforms, able to connect to a multitude of patient databases and to analyze a complex mixture of data types (that is blood pathology, genomics, radiology images, medical history) are the need of the hour. Furthermore, these systems should be able to sift through the analyses in a deep manner and discover the hidden patterns.
- Physical robots for surgery assistance – Surgical robots can provide unique assistance to human surgeons, enhancing the ability to see and navigate in a procedure, creating precise and minimally invasive incisions, causing less pain with optimal stitch geometry and wound. There are truly exciting possibilities for the application of AI/ML for such digital surgery robots.
- AI for healthcare operation management and patient experience – AI and associated data-driven techniques are uniquely prepared to tackle some of the problems, identified as the root causes of long queue, fear of unreasonable bills, the long-drawn and overly complex appointment process, not getting access to the right healthcare professional, etc.
- Drug discovery with the aid of AI/ML techniques – AI techniques are increasingly being applied to accelerate the fundamental processes of early-stage candidate selection and mechanism discovery. For instance, biotechnology company Berg uses its AI platform to analyze immense amounts of biological and outcomes data (lipid, metabolite, enzyme, and protein profiles) from patients to highlight key differences between diseased and healthy cells and identify novel cancer mechanisms.
- Precision Medicine and Preventive Healthcare – Finding precise treatment options for an individual based on his or her personal medical history, lifestyle choices, genetic data, and continuously changing pathological tests. Naturally, we need to bring the most powerful AI techniques, deep neural networks, AI-driven search algorithms/advanced reinforcement learning, probabilistic graphical models, semi-supervised learning to tackle the challenge. AI-system can also potentially predict future patients’ probability of having specific diseases given early screening or routine annual physical exam data. Moreover, AI tools might be able to model why and in what circumstances diseases are more likely to occur, and thereby, can help guide and prepare doctors to intervene (in a personalized manner) even before an individual starts showing symptoms.