Healthcare has been one of the quickest growing sectors of the economy during the last 10 years. And, with the increased threat of pandemics like the coronavirus epidemic, the industry is sure to expand. Organisations throughout the world are turning to innovative approaches based on artificial intelligence (AI), machine learning (ML) and big data to remain ahead of the game in healthcare.
AI provides significant advantages over earlier methods of analysis and clinical decision-making systems. Learning algorithms are growing more effective and reliable as they interact with better quality training data, allowing humans to gain unprecedented insights into diagnosis, treatment plans, treatment variations and patient outcomes.
Potential Advantages of AI and Big Data for Consumers and Businesses
- Using chatbots to enable patient self-service
- Employing computer-aided design to diagnose patients more quickly
- Image data analysis is used in drug discovery to evaluate the molecular structure and radiologists use this technology to assess and diagnose patients
- Using more comprehensive clinical data to personalise therapies
How AI and Big Data Have Been Impacting the Healthcare Industry?
- Big data technologies are now being utilised to fight cancer. With various technologies such as distributed file systems, we can analyse clinical data to uncover hidden patterns that lead to earlier cancer detection. The earlier diseases such as cancer are discovered, the higher the odds of successfully treating them. Big data technologies are excellent in analysing genome sequences to identify biomarkers for cancer, as well as revealing populations at increased risk for cancer and uncovering previously unknown therapies. Big data approaches are being used by the most forward-thinking firms to speed up studies and generate therapies that are speedier and have more concrete outcomes.
- By employing artificial intelligence and machine learning tools to spot abnormalities that a human eye would miss, we are improving diagnosis and preparing patients for improved patient care. Following are a few examples:
- Stanford researchers developed an algorithm for interpreting chest X-rays that is as accurate as radiologists. With this technique, they can interpret data in a quarter of the time.
- Doctors who use AI save important hours of diagnosis time by swiftly and reliably detecting blood clots in patients with stroke before serious harm is done.
- Radiological pictures produced by MRI machines, CT scanners and X-ray equipment provide non-invasive access to the working of the human body. However, many diagnostic techniques still rely on actual tissue samples collected through biopsies, which involve dangers such as infection.
Experts expect that artificial intelligence will allow the next generation of radiological tools that are precise and thorough enough to replace the requirement for tissue samples in some circumstances. If this search is successful, practitioners will be able to create a more precise picture of how tumours behave as a whole, rather than making treatment decisions on the features of a particular portion of malignancy. Physicians may also be able to more accurately determine the aggressiveness of tumours and focus therapies accordingly.
- The migration of repetitive and unnecessary manual work to robots holds the key to unravelling the existing healthcare system’s cost structure problem. There is a need to allow patients to self-serve their treatment requirements as much as feasible. This has the potential to reduce the amount of human labour necessary to maintain more people living healthier lives.
- Robots are now utilised in a variety of industries, including manufacturing and warehousing. However, robots are increasingly being employed in hospitals and many of them are AI-enabled. Physical robots are becoming more collaborative with people and maybe programmed to do a variety of jobs using AI reasoning. It is not simply about transporting supplies to hospitals, but robots can also help surgeons see better and make more accurate and least invasive incisions, suture wounds and so on. Robots can enhance the productivity and efficiency of a broad variety of medical services by using AI to drive their decision-making processes.
- Traditionally, physicians would handwrite or type assessments and clinical records and no two did it the same way. They would frequently conduct it after the patient’s visit, which invites human mistakes. Conversations with patients, clinical diagnoses and prospective therapies, on the other hand, maybe supplemented and documented more correctly and in near real-time with AI- and deep learning-powered voice recognition technology.
- Discovering the source of illegitimate prescriptions and closely monitoring it is a new problem or task. There have been several instances of healthcare fraud and abuse, such as the prescription and distribution of medicines. Machine learning can assist in the detection of unusual and possibly fraudulent providers, which would be challenging and time-intensive for humans to filter through and find. Healthcare firms may use machine learning programmes and algorithms to identify trends in data and when they depart from those patterns. This enables healthcare companies to change from a “pay and pursue” approach to one of prevention and detection.
- Almost all customers now have the availability of gadgets equipped with sensors that may collect useful health data. A rising share of health-related data is generated on the road, from cell phones with step monitors to wearables that can monitor heartbeat round the clock.
Collecting and analysing this data and complementing it with information supplied by patients via apps and other personal monitoring devices can provide a unique perspective on individual and public health. Artificial intelligence will be crucial in deriving useful insights from this vast and diverse treasure trove of data.
The Final Word
As of today, some people are still wary about adopting technologies (AI) in full force without a huge body of evidence to back up the results. While there are several use cases and proof of the promised benefits of employing AI and ML applications in the healthcare business, there are numerous obstacles for healthcare and tech firms that go beyond just improving technology to serve a vast potential market. AI will bring about a new age of clinical excellence and exciting advancements in patient care by powering a new generation of tools and systems that make doctors more conscious of subtleties, more efficient when giving treatment and more likely to get ahead of developing issues.
Also Read: What Are The Benefits Of Digital Healthcare?