Medical error is the third leading cause of death after heart failure and cancer. In a recent study on medications, it says that there are medication errors, including delayed treatment all over the globe. Still, the problem of people dying from medical care gone wrong has been vastly underappreciated and not well recognized. Today, we stand on the threshold of the new medical revolution.
Artificial intelligence (AI) aims to replicate human cognitive functions. It is bringing a model to healthcare, powered by increasing availability of healthcare data and rapid progress of analytics techniques. According to the survey of current status of AI applications in healthcare, AI can be applied to various types of healthcare data.
It was observed that for the past few years, things are evolving within the clinical trial space, and is having an impact on clinical data management. There has been a shift in how clinical evidence is produced and where it's produced.
Popular AI techniques include machine learning methods for structured data, such as the classical support vector machine and neural network, and the modern deep learning, as well as natural language. Major disease areas that use AI tools include cancer, neurology and cardiology. AI also has applications in stroke, in the three major areas of early detection and diagnosis, treatment, as well as outcome prediction and prognosis evaluation.
There have been traditional clinical trials, but close observation is needed in things like pragmatic trials or synthetically controlled models and how to deal with such data. We need to look at the data and see how we can gain value from analysing the data.
The data itself is changing with time as well. The type of patients increases as more cases are taken to study. It's not just the traditional data there's a lot that can be obtained from omics data to tracking data.
Machine learning is one solution that has been gaining grip in other industries outside of the healthcare department.
Machine learning is an increasingly viable option for more data. Data and the source of data are increasing. Omics data and tracking data in a real-world setting mean there's value in increasing the number of patients involved in a given trial. Clinicians and researchers are trying to study the data together.
Over the years, biotechnology has evolved immensely. Computers are becoming faster in speed and micro in size, heterogeneity is increasing in datasets and their volume is growing robustly. These expansions are fuelling the engine of artificial intelligence (AI) for discovering many technical refinements to solve complex problems in almost every field of life, including science and medicine. One of the expected roles in life and medical sciences is to deal with extensive research studies aimed at supporting real-time decision-making and producing solutions to complex problems through knowledge and data intensive computational and simulated analysis.
In the recent past, multiple AI and ML-based efforts have been made for deciphering diseases to facilitate predictive diagnosis and thereby guide treatment factors, e.g. drawing disease relationships using clinical manifestations, EHR and data generated using wearable technology.
AI and ML algorithms have contributions in healthcare and approaches. Contributions of AI and ML are divided into three categories:
• Health Intelligence
• Precision Medicine
• Healthcare Resource Management and Ethical Challenges.
The idea of embracing changes with the advancement in technology with the potential integration of AI into the field of healthcare in a way that is beneficial to each healthcare worker. They focused on utilizing AI to obviate repetitive tasks to enhance patient–physician relationships and increase practice of empathy and emotional intelligence.
Implementation of AI and ML in healthcare for the extraction of big data, and aiding clinicians in providing better care delivery. Gaining insights about the patient journey helps to understand how a particular drug is affecting the patient.
There are new techniques that have been enabled by machine learning and the data and a data manager thinks about how to provide that data. Not one database holds all the data. Life science industry looks into healthcare as a commercial space, and is processing the data accordingly.
Data management group looks at how data is processed to match the big tech companies.
The life sciences industry is really conservative. It is slow in adopting, even when regulators encourage it, hesitancy comes from a fear of job loss and automation. Although automation is needed because the entry of data never stops.
