Artificial Intelligence (AI) is offering truly life-changing advantages in healthcare, just like it is revolutionizing many other industries with its transformative power to process information accurately. AI has countless advantages for the health care sector in discovering links between genetic codes, developing highly efficient surgical robots, and enhancing hospital efficiency. In addition, the benefits of AI in pharma manufacturing has been ground-breaking.
The total consolidated (private and public sector both) investment in AI healthcare is mind-blowing and it is expected to reach 6.6$ billion by 2021. AI sector is often being touted as one of the highest-growth industries of the world. Take a look at the staggering growth statistics for the real picture- the AI industry was valued at about $600 million in 2014 and is projected to reach $150 billion by 2026.
Top technology companies IBM and Google have also developed specific AI algorithms for healthcare. Furthermore, several top medical institutions like The Mayo Clinic, Memorial Sloan Kettering Cancer Center, and the British National Health Service have specially designed and developed AI algorithms for their departments.
As per Wikipedia- Artificial intelligence (AI) in healthcare is the use of complex algorithms and software to emulate human cognition in the analysis of complicated medical data. Specifically, AI is the ability of computer algorithms to approximate conclusions without direct human input.
AI technology is quite superior to other traditional technologies in its execution of information for healthcare because of its ability to acquire the information and process it to give a well-defined output to the end-user.
AI enables humans to gain unprecedented insights into diagnostics, treatment variability, patient outcomes, and care processes by providing accurate and more precise training data through learning algorithms and deep learning.
These learning algorithms and deep learning can recognize patterns in behavior by creating their logic in such a manner that it reduces the margin of error by testing the algorithms repeatedly.
Python one of the major languages used primarily in AI application development in healthcare. AI programs for practices like diagnosis, drug development and insurance, treatment protocol development, personalized medicine, and patient monitoring to reduce spending and improve patient outcomes.
Hospitals are developing specially designed AI software for supporting their operations to increase cost-saving and improve patient satisfaction. Similarly, companies are looking for predictive analysis solutions to optimize business operations via increasing utilization, reducing stay length, and optimizing staffing levels.
Simply put, Artificial intelligence simplifies tasks that are typically done by humans in less time and at a fraction of the cost.
Overall, looking at the present trends, better facilities, newer inventions, and investments each day in healthcare, it would be perfect to say that Artificial Intelligence is the newly found new nervous system for the Healthcare industry, that is poised to be the torchbearer driving force for improvements in the care continuum.
Major Healthcare applications of AI in Medical and technological advancements are:
Other future applications for AI include Brain-computer Interfaces (BCI), which can help people with trouble moving, speaking, or with a spinal cord injury. The BCIs will use AI to help such patients move and communicate by decoding neural activates. Brain-computer interfaces can significantly improve the quality of life for patients with ALS, strokes, or locked-in syndrome. Also, people who experience spinal cord injuries every year would greatly benefit from BCI.
Let us know more by separately understanding following technologies that are most likely to witness a major impact from artificial intelligence within the next few years:
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Misdiagnosing and medical errors account for a large number of deaths and other serious misinterpretations in the field of health industry. There are many dreadful serious illnesses such as Diabetes, and Cardiovascular Disease (among the top ten diseases for causes of death worldwide), where Artificial Intelligence is efficiently used to accurately diagnose the illness. Due to the high mortality rate of these diseases, there have been intense efforts to integrate various methods in helping get an accurate diagnosis for these diseases.
Providing efficient and accurate diagnostic process is one of the major achievements of AI in healthcare applications, where AI can predict and diagnose faster than any medical professionals.
It has been found in one of the research studies that, an AI model using algorithms and deep learning diagnosed breast cancer faster than 11 pathologists.
AI technology is helping skin cancer detection system by accurately diagnosing cancer by deep learning convolutional neural network (CNN Machine), rather than by dermatologists.
On average, the human dermatologists accurately detected 86.6% of skin cancers from the images, compared to 95% for the CNN machine.
The ability to interpret imaging results with radiology help scientists in detecting changes in the image that might get missed by human monitoring. All such processes are efficient through deep learning to detect specific diseases and disorders in the human body. Medical imaging solutions offered by modern companies such as Novarad help healthcare executives, clinicians, and IT professionals deliver clinical, operational, and fiscal excellence.
Also, some of the specialists feel threatened by the superiority of AI technology in radiology, as the technology offers better improvements in statistical metrics in isolated cases, as opposed to specialists.
Radiological images obtained by MRI machines, CT scanners, and X-rays offer non-invasive visibility into the intrinsic details of the human body. Still, today some diagnostic processes rely on physical tissue samples(obtained through biopsies), which carry risks for infections.
Electronic health records are crucial for digitalization & effective communications of the healthcare industry. EHR developers have started automating the process to a large extent and are using NLP tools for better process experience.
Sometimes, logging all of the data comes with problems like cognitive overload and burnout for users.
Drug-drug interactions pose a great threat to consuming multiple medications simultaneously and the danger increases with the number of medications. To address this difficulty of tracking suspected drug-drug interactions, machine learning algorithms are found incredibly useful in extracting useful pieces of information on drug interactions and their effects from medical literature.
Improvements and advancements in NLP (natural language processing) led to the development of algorithms to identify drug-drug interactions more accurately in medical literature procedures.
The widespread use of telemedicine has given a considerable rise of AI applications. The ability to monitor patients using AI may allow communicating of information to physicians. AI is greatly helping serve the purpose via wearable technology where a wearable device allows constant monitoring of the patient and capturing the minutest of details and changes that have fewer chances of getting noticed by human monitoring.
Artificial Intelligence is greatly helping the healthcare services by new inventions and discoveries of useful drugs for notorious ailments. Biopharmaceuticals companies are taking notice of the enormous potential of AI technology in saving development and research costs by providing accurate, efficient results in lesser time.
In psychiatry, AI applications are in a phase of proof-of-concept. The main focus areas where the evidence is widening are chatbots, conversational agents that imitate human behavior (which have been studied for anxiety and depression).
The major challenges here are that many applications are developed by private corporations, like the screening for suicidal ideation (by Facebook in 2017). These applications outside the healthcare domain raise professional, ethical, and regulatory concerns.
Health companies collaborating with similar domain companies, allow for greater health data accessibility and smoother implementation of AI algorithms.
A large part of the industry focus of AI in the healthcare sector is in the clinical decision support systems. As the number of data increases, the AI decision support mechanism becomes more efficient. Besides, many companies are exploring the possibilities of incorporating big data in the health care industry.
The following top companies that have contributed to AI algorithms in healthcare are:
IBM’s Watson Oncology is working at Memorial Sloan Kettering Cancer Center and Cleveland Clinic. Besides, they are working in partnership with CVS Health in developing AI applications for chronic disease treatment and in alliance with Johnson & Johnson to discover new findings for drug development.
Microsoft is working in partnership with Oregon Health & Science University’s Knight Cancer Institute for Hanover Project, analyzes medical research to predict the most effective cancer drug treatment. Also, they are working on medical image analysis of tumor progression and the development of programmable cells.
Google’s Deepmind platform for the UK National Health Service to detect certain health risks through data collected via a mobile app. Additionally, they joined hands with NHS for analysis of medical images collected from NHS patients to develop computer vision algorithms to detect cancerous tissues.
Tencent is working on several medical systems and services in AI Technology like:
Intel’s Intel Capital invested in startup Lumiata which gathers AI information to identify at-risk patients and develop care options via super-efficient AI techniques.
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