Editorial Summary :
Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on employing algorithms to learn from data without the need for additional programming . 50% of hospitals in the US, UK, and Germany have an AI framework to support their business . Machine learning is also used by pharmaceutical companies to aid in drug discovery and development . Nearly a quarter of respondents (24%) already have AI/ML efforts in their healthcare systems at the pilot stage, according to another poll by Statista of US healthcare providers . Up to one-third of all healthcare AI SaaS businesses are primarily or solely focused on diagnostics . Machine learning can help clinicians correctly detect diseases and improve the quality of treatment . The ability to deliver pharmaceuticals more quickly to those who require them is the biggest advantage . For instance, the smart tissue autonomous robot (STAR) from Johns Hopkins University has already proven that it can do surgical tasks like suturing and knot-tying better than human surgeons . It’s still too early to discuss robots doing all surgical procedures, but they can help doctors a lot when it comes to manipulating surgical instruments . The market for AI in healthcare was assessed at $6.7 billion in 2020, and from 2021 to 2028, it is expected to increase at a CAGR of 41.8 percent . The application of machine learning to disease prediction in healthcare is one of the most striking examples . IBM Watson Genomics uses cognitive computing and genome-based tumour sequencing to accelerate accurate cancer diagnosis . MedInReal offers a virtual care assistant for doctors based on AI, which can use NLP capabilities to update EHRs and automate routine procedures . The market is anticipated to lead and expand due to the application of machine learning, and more specifically, deep learning, in healthcare applications . The cost benefits to the healthcare industry are a significant factor in the massive adoption of ML . By 2026, AI applications could possibly save the US healthcare system $150 billion annually, according to Accenture estimates . There are issues in applying machine learning in the medical field. Some market participants think it might result in the reduction of medical staff. By relieving medical professionals of regular, boring activities, active ML usage would assist reduce overwork among the dwindling healthcare workforce in across nations .
Key Highlights :
- Machine learning is a branch of artificial intelligence that focuses on employing algorithms to learn from data without the need for additional programming .
- Machine learning can carry out human-like tasks because of its capacity to adapt to new inputs and learn from experience .
- Machine learning can help doctors better diagnose diseases and improve the quality of treatment .
- It can also help identify patients’ conditions and assess data in real-time .
- Machine learning algorithms can also be used to assess data on patients’ medical history .
- Machine learning can help predict disease and predict disease outcomes .
- IBM Watson Genomics uses cognitive computing and genome-based tumour sequencing to accelerate cancer diagnosis .
- Machine learning is being used in medical imaging, disease detection, and medication discovery .
- By 2026, AI applications could possibly save the US healthcare system $150 billion annually .
The editorial is based on the content sourced from medium.com