Machine Learning is revolutionizing Healthcare-Is this the Future ? | Sparkout Tech
The healthcare sector has long been an early adopter of technological advancement. Machine learning, a subset of artificial intelligence, presently plays an important role in many health innovations, such as the development of new medical procedures, the handling of patient data, and the treatment of chronic diseases.
Machine learning is used in a wide range of health-care applications. According to a Mercury News report, machine learning and AI are expected to play a critical role in central nervous system clinical trials in the future.
Other prominent machine learning developments in healthcare, such as telemedicine and a few machine learning companies, are analysing how to organise and deliver patient information to doctors during telemedicine sessions,as well as gather information during virtual visits to streamline workflows
Machine learning is also used by pharmaceutical companies to help in drug discovery and development. For example, machine learning could one day help drugmakers to predict how patients will react to various drugs and identify which patients are most likely to benefit from the drug. Meanwhile, the Food and Drug Administration in the United States has passed a few policies that allow medical devices to use AI and machine learning technologies.The following discussion helps you get a detailed Insights of Machine Learning in Healthcare.
Given all of these applications, we constructed a list of 10 healthcare companies that use machine learning.
Machine Learning in Healthcare Examples
- Microsoft
- Tempus
- PathAI
- Kareo
- Beta Bionics
- KenSci
- Ciox Health
- Subtle medical
- Pfizer
- Insitro
1. Microsoft
Founded: 1975
Redmond, Washington.
Microsoft's Project InnerEye uses computer vision and machine learning to distinguish between tumours and healthy anatomy in 3D radiological images, assisting medical professionals in radiotherapy and surgical planning. Microsoft's AI-based approach aims to produce medicine that is tailored to the specific needs of each patient.
2. Tempus
Founded: 2015
Chicago, Illinois
Tempus strives to advance cancer research by gathering massive amounts of medical and clinical data in order to provide patients with personalised treatments. Analyzing its data library with an AI-powered algorithm, Tempus helps with genomic profiling, clinical trial matching, diagnostic biomarkers, and academic research.
3. PathAI
Founded: 2016
Boston, Massachusetts
The technology developed by PathAI uses machine learning to assist pathologists in providing faster and more precise diagnosis. The company also provides AI tools for collecting patient data, processing samples, and streamlining other clinical trial and drug development tasks. A partnership network of biopharma communities, labs, and clinicians equips PathAI with the resources to give more effective treatments for patients.
4. Kareo
Founded: 2004
Irvine, California
Kareo provides a cloud-based clinical and business management platform to support the tech and business needs of independent practises. Organizations can transfer patient health and financial data to Kareo's billing platform, making record management and transaction completion easier. Furthermore, Kareo uses AI technology to automate repetitive tasks, cutting down even more time and operational costs for practitioners.
5. Beta Bionics
Founded: 2015
Boston, Massachusetts
Beta Bionics is developing iLet, a wearable "bionic" pancreas, to make diabetes patients' lives easier. This device, which is still in the research stage, constantly monitors blood sugar levels in Type 1 diabetes patients, relieving them of the burden of tracking their blood glucose levels on a daily basis.
6. KenSci
Founded: 2015
Seattle, Washington
KenSci predicts illness and treatment using machine learning, allowing physicians to intervene earlier and help patients avoid potentially serious events. Healthcare professionals can also predict population health risk using KenSci's analytics by identifying patterns and surfacing high-risk markers, as well as modelling disease progression.
7. Ciox Health
Founded: 1976
Alpharetta, Georgia
Ciox Health uses machine learning to power its Datavant Switchboard platform, giving healthcare professionals faster access to patient data. Within the platform, organisations can create personalised controls that allow employees to submit requests for specific types of data. Ciox Health's technology also adheres to privacy compliance standards in order to safeguard patients' electronic health records.
8. Subtle medical
Founded: 2017
Menlo Park, California
Subtle Medical uses AI, machine learning, and deep learning to produce clearer medical images for radiologists. The company's product, SubtleMR, is able to block out image, noise and focus on areas such as the head, neck, abdomen, and breast. Higher-quality images allow radiologists to finish exams more quickly, reducing the time it takes for patients to receive care and diagnoses.
9. Pfizer
Founded: 1848
New York
Pfizer uses machine learning and natural language processing with IBM's Watson AI technology for immuno-oncology research into how the body's immune system can fight cancer. This collaboration allows Pfizer to analyse large amounts of patient data and gain faster insights into how to develop more effective immuno-oncological treatments for patients.
10. Insitro
Founded: 1975
San Francisco, California.
Insitro combines machine learning and computational biology to improve the efficiency and cost-effectiveness of drug development. Following the development of predictive models from massive biological data sets, the company employs machine learning to sift through this data and uncover important trends, such as new disease subtypes. Insitro's medical professionals can then adjust drugs and medicines to better protect patients from evolving diseases.
Summing up
In the years to come, machine learning's effect on healthcare will undoubtedly grow. Therefore, clinicians and healthcare professionals need to start using machine learning to their benefit. The highest score for machine learning in healthcare goes to its powerful abilities in sorting and categorising health data, as well as speeding up doctors' clinical decisions and any types of visions that can save lives or make surgery less complicated.