Data is the heart of medicine. A well-organized and trusted data source fosters a faster and more accurate healthcare system. A significant transformation in the healthcare sector is brought about by new technologies namely, Artificial Intelligence and Machine Learning.
The Healthcare industry being an early adopter of technology has been able to avail benefits of AI & ML. This blog will shed some light on the implementation of AI and ML in the industry.
Introduction
Healthcare organizations have to analyze piles of data, images, reports, clinical trials, etc in order to conclude the exact cause of an illness. Human skills often fail in finding out important insights from a large set of data.
Misdiagnoses may lead to unnecessary tests and time consumption. The delay in treatment could put the survival of patients into question.
During such cases, these finest technologies, Artificial Intelligence and Machine Learning come to the rescue. The profound impact of automation these technologies bring to the healthcare system enhances the quality of decision-making for healthcare providers. The accuracy and swiftness with which AI and ML leverage can possibly save millions of lives.
How does AI function in healthcare?
The Healthcare system has data in abundance. However, the data pool is distributed and has different ownership which puts them at risk of fraud. Due to such vulnerabilities, the data extraction efficiency is often put into question by professionals.
AI and ML function based on algorithms. The more these algorithms are exposed to data, the higher accurate results they can provide. These algorithms analyze the data storage system and unearth important insights and patterns from it. Natural Language Processing (NLP) further isolates the data according to its relevance.
The extraordinary potential of AI and ML gives impetus to healthcare functioning. Some popular names like IBM and Microsoft have made their shifts to AI healthcare projects already.
A McKinsey report forecasts that big data would help healthcare save nearly $100Bn annually. This sounds like a blessing in disguise for problem-solving in patient care.
In the next section let us explore some cases where AI and ML can be used for problem-solving.
Breast cancer and other forms of Cancer Diagnoses:
Cancer cases are multiplying every year. Breast cancer is the primary contributor to the numbers. Deep learning and AI give a breakthrough in cancer analysis by helping medical professionals analyze massive amounts of medical data.
The swift analysis enables delivering personalized medical treatments. Machine Learning algorithms back the MRI & medical imaging systems. All these analyses fall just right in place for improving the accuracy of breast cancer detection.
Surgeons can make use of ML tools to know the precise location of cancer during robotic procedures.
Artificial Intelligence and Machine Learning used in Radiology & Pathology:
What radiologists interpret throughout the day is unimaginable. If we believe the Nature article, it says, a radiologist’s job demands analyzing one image every three to four seconds!
There comes into the picture the new assistant, AI, and DL. AI can act as an assisting technology in radiology. This technology has high potential in transforming the way human research studies are conducted. The data sets are fed into the algorithms and the process helps in scanning images and thereby identifying patterns.
The outperforming algorithms help radiologists in spotting malignant tumors. The highly informed radiologists can thereby easily spot the damaged spots more quickly.
Hence we can say, the potential of ML can thus be utilized in treating rare and difficult-to-diagnose diseases.
Drug Discovery Using Machine Learning and Artificial Intelligence:
Pharma companies have started using machine learning for drug discovery and development. Surprised how? The powerful technology will assist drug makers to predict the way patients behave to various drugs. Further, based on the data, they can decide what dose works best for the patient with a given illness.
The implementation will help pharma companies save nearly 2.5 billion dollars that they spend over the entire research & launch. Having said that, the US Food and Drug Administration has laid out a few policies regarding using AI and ML for this segment.
Here are the names of a few companies using Machine Learning in the healthcare department:
- Microsoft;
- Tempus;
- Kareo;
- KenSci;
- Pfizer;
- Subtle Medical;
- Beta Bionics;
Some of the big names in pharma companies have also grabbed the opportunity of using this dynamic technology to resolve difficult health problems.
- Genentech uses GNS Healthcare, Cambridge, Massachusetts AI system to run a search for various cancer treatments;
- Pfizer uses the IBM Watson platform to hunt immuno-oncology drugs.
- Also, Sanofi plans to use Exscientia’s AI platform for finding metabolic-disease therapies;
This proves that AI and ML will bring many amendments to the way startups function in drug research.
AI can be used in analyzing forecast kidney disease:
Kidney diseases can become life-threatening if not treated at the right time. Patients' health in such cases starts deteriorating very fast and this can possibly lead to instant death.
An early prediction can save lives. ML tools can really be helpful during such scenarios. The Department of Veterans Affairs and DeepMind Health developed an ML tool that helps in foreseeing Acute Kidney injury nearly 48hours in advance. The AI-ML tool was successful in recognizing more than 90% of acute kidney diseases beforehand.
The experiment is still in continuation and the involved parties are planning to take it to the next level by installing ML tools in medical units.
AI analyzes unstructured data:
The primary role of medical professionals is to provide accurate treatment to patients. In keeping up the pace, they often struggle to keep themselves updated with the latest research and advances in the field.
The struggle is largely reduced when these new technologies start playing their roles. ML tools swiftly scan EHRs & biomedical data and provide results promptly. The results are highly reliable and help in interpreting data nicely.
The future of AI in healthcare:
Challenges are part of evolution. As discussed, healthcare app development companies and start-up owners would have to work hard in unveiling the potential of AI & ML. But, one good thing is, the curtains have already been raised. Hence, it would just be research work for a few more years before AI & ML become full-fledged assistants in healthcare.
If you need our help in AI implementation, please feel free to ring our bells. We would love to help you in creating a classic healthcare app development strategy. Thank you!