Examples of Machine Learning Applications in Healthcare
In the rapidly evolving world of healthcare, machine learning is making a significant mark, revolutionising various aspects of the industry. From genetic design to early disease detection, the innovative technology is being utilised in unique and groundbreaking ways.
Tebra's Kareo product, for instance, offers a cloud-based clinical and business management platform for independent practices. By automating repetitive tasks with AI technology, it streamlines operations and enhances efficiency.
Machine learning is also playing a crucial role in telemedicine, with companies studying how to organise and deliver patient information to doctors during remote consultations. This development promises to make telemedicine sessions more effective and efficient.
BioSymetrics, on the other hand, is helping organisations analyse large amounts of raw data to streamline the development of precision medicine. Their machine learning platform and contingent AI provide more comprehensive insights on how to improve medicines, paving the way for personalised treatment plans.
Predicting patient responses to certain drugs is another area where machine learning excels. This capability could potentially revolutionise the way medications are prescribed, ensuring they are more effective and minimising adverse reactions.
PathAI uses machine learning to aid pathologists in making quicker and more accurate diagnoses. Their AI tools compile patient information, process samples, and streamline tasks for clinical trials and drug development.
Ciox Health's Datavant Switchboard platform, powered by machine learning, gives healthcare professionals faster access to patient data. By following privacy compliance rules, staff can submit requests for specific types of data, making healthcare operations more efficient.
Beta Bionics has developed a wearable "bionic" pancreas called iLet for diabetes patients. This innovative device constantly monitors blood sugar levels, reducing the burden of tracking blood glucose levels on a daily basis.
ConcertAI uses machine learning to analyse oncology data, providing insights that allow oncologists, pharmaceutical companies, payers, and providers to practice precision medicine and health.
Orderly Health serves organisations with a B2C concierge chatbot that interacts via text, email, Slack, and video conferencing. By making it easier to understand benefits and locate the least expensive providers, the chatbot aims to help employers and insurers save time and money on healthcare.
Pfizer uses IBM's Watson AI technology for immuno-oncology research. This collaboration enables faster insights on how to produce more impactful immuno-oncological treatments for patients.
Subtle Medical taps into the potential of AI, machine learning, and deep learning to produce clearer medical images for radiologists, reducing the time it takes for patients to receive care and diagnoses.
In the United States, a host of companies are leveraging machine learning in healthcare. Epic, for example, develops software for personalised patient care and data management. Insilico Medicine, a clinical-stage biotech firm, uses generative AI for drug discovery. GE Healthcare, Allscripts Healthcare, Cerner, and Fitbit are also harnessing the power of machine learning.
Consulting and analytics firms like ZS contribute by implementing AI-driven platforms to improve healthcare operations and biopharma commercialization.
Innovative companies like Insitro combine machine learning and computational biology to make drug development more efficient and cost-effective, adjusting drugs and medicines to better protect patients from evolving diseases.
Machine learning has also impacted drug discovery and development for pharmaceutical companies. Companies such as Microsoft, Tempus, Tebra, PathAI, Ciox Health, Beta Bionics, Subtle Medical, Pfizer, Insitro, BioSymetrics, Asimov, Strive Health, Definitive Healthcare, Evidation, Cohere Health, GRAIL, Novo Nordisk, Linus Health, and Microsoft are all contributing to this transformation.
As machine learning continues to evolve, its potential applications in healthcare are boundless. The future of healthcare looks increasingly promising, with personalised, efficient, and effective care on the horizon.
