Artificial intelligence is instrumental as one of the most effective solutions in modern-day healthcare technology. Its financial growth is getting more robust with more promising solutions. It involves combinations of various technologies that allow machines to mimic healthcare professionals both clinically and administratively.
AI can be trained with the thought and decision process of medical professionals. It can sense, observe, learn, record, analyze, interpret, and act on medical data. These abilities of AI will complement human efforts and increase the medical success rate. The advantages of AI will enable humans to achieve precision medicine.
“Precision medicine means that we need to deliver in the context of workflow decision support to the clinician to do the right thing at the right time.”
— John Halamka
It is almost an impossible quest for humans in the medical sector to keep abreast with the increasing inflow of information about health conditions, treatments, and medical technology.
This is a good reason for the further development of artificial intelligence in healthcare. The use of smart health tools is also another expansion opportunity for AI and machine learning as it helps in solving health challenges.
Below are applicable ways that AI is influencing health matters:
Research and Development
AI can help in the discovery of new medications based on records of prescribed drugs and medical intelligence. The use of big data and AI can assist in investigation and discovery of new medications for specific illnesses, and the result will be a big plus for pharmaceuticals.
Comparative effectiveness of drugs and medical devices can be advanced by the use of AI. Deep machine learning can choose the most applicable information from data records for experimental design to indicate the best medical solutions.
The root genetic cause of ailments in humans can be researched by biotech companies using AI. Gene components and analysis will be understood better. The further use of AI could help in forecasting the results of gene editing.
Medical Imaging and Diagnostics
Radiology spans imaging techniques, such as X-rays and treatments like radiation therapy while Radiography is restricted to performing the actual imaging tests. These tests are usually X-rays, CT scans, and MRI procedures.
Medical imaging is a great fit for AI adoption, the use of computer vision technology can help intelligent systems to observe photographs or results of scans. The application of deep learning can interpret images in details.AI in radiology is already being used for reading images, but a doctor still needs to review the results. Click To Tweet
An example is the case of the University of Rochester Medical Center. The medical center announced its application of AI to identify and prioritize the urgency of ill patients and schedule who sees the doctor first.
AI also helps to diagnose skin cancer more accurately than human experts with the use of skin images. This has lowered the cases of false positives in assessing symptoms, allowed to reduce the waiting list for surgery and make sure that only real patients get treatments.
The availability of a very rich database and application of deep structured learning is a superior combination for digital consultation. This is because deep learning is a method based on learning data representations other than using algorithms that are task specific. In this case, deep learning enables the system to make well-informed decisions based on millions of cases that are relevant to the case of a specific patient.
Advanced natural language processing is also a viable option for digital consultation in healthcare. It is able to understand complicated sentences other than the selection of predefined options.
Advanced natural language processing is simply the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. Real-time AI conversational analysis together with deep structured learning will solve the problem of answering patients questions and recommend the best action.
Personal Medical Experience
People with specific family medical history and records can get highly detailed diagnosis and treatments. AI can consider risk factors like allergies and genetics to make treatments better. Unlike other personalized medical options, AI can be superior as more data collections are actualized using learning models.
Home-use AI driven diagnosis is still in the making, but successful and interesting tests are being made. A good example is Remidio, by analyzing the photos of a patient’s eye, a mobile phone diagnosis of diabetes is possible.
With the existence of an applicable dataset in AI, personalized medication could analyze a person’s gene and chromosome to decide the best treatment, however, such a dataset must be created first.
Cybersecurity and Blockchain
We can’t look away from the risk of hackers as many AI solutions are functional with the internet. The connectivity to the internet can be a room for cyber-attack and hospitals are not let out. Stakeholders in the medical field are adopting stronger cybersecurity policies.
AI can solve the problem of cybersecurity. Advanced solutions are fashioned by the use of machine learning to observe and understand unusual network behavior. It can also fish out and block abnormal or anomalous activities by indicating attacks or vulnerabilities.
The application of Blockchain in medical AI can help in securing medical data storage and its management. The trust of blockchain and AI in medical data analytics will be of value in securing and permitting users to extract data. It will also make the process of data storage in hospitals transparent and secured with cryptography.
Medical data records can be of great use beyond average data management. Using AI, the data obtainable from health records can be used in the analysis of price and risk management of medical services based on competition and market conditions.
Marketing research of pharmaceuticals can be facilitated too, as well as automating everyday office and administrative operations in medical centers especially report generation.
Health Predictions and Forecasts
During coma, AI can analyze brain scans and indicate in its results the possibility of recovery and influence the withdrawal of life-support. A Chinese trial has been able to accurately predict exit from coma where human doctors could not foresee one. AI was 90% accurate by tracking blood flow to the brain and any other details omitted by the human eye.
“It will never replace doctors. It is just a tool to help doctors and families make better decisions.”
— Dr. Song Ming
Wearable Health Devices
Considering the implication of data cost especially in the health sector, it’s worth investment by tech companies looking for opportunities in wearable health device. There are lots of opportunities if we consider the volume of health data that can be harvested in an individual’s lifetime using wearable devices.The use of intelligent devices can give more flexibility and alternatives to usual health assessment. Click To Tweet
Some of these health assessments usually require visiting a doctor. Examples are diabetics, blood pressure, Parkinson’s disease, multiple sclerosis etc.
The use of intelligent wearables can allow data sharing with doctors. For example, the introduction of ECG/EKG wearables by Qardio, an AI health company, reveals an effective method of collecting the most needed information for diagnosis at a short interval. This technology can also help to reduce the premiums for health insurance.
Companies are now making intelligent wearable devices for runners and other forms of light sports or exercise. An example is the “smart sock” embedded with sensors. This device is readily substituting the usual hospital machinery for measuring postural way or likelihood of falling at a lower cost.
The general applications and possible use of Artificial Intelligence in healthcare are growing as well as improved solutions. From the complexity of robot surgeons to the use of robot chats to cure depression.
The great union of AI and ML in the healthcare industry are very promising. This is due to the large data sets and records available from over the years. ML patterns can learn to detect ailments, suggest the diagnosis, and even predict the duration from convalescence to total healing.
The health industry is a place for no risks and recklessness hence the implementation and use of technology such as AI must be top-notch. The purpose of using AI is to effectively save lives, therefore much effort must go into improving, perfecting, deploying, and regulating the use of such technology.
Velvetech is a reputable company in AI and ML projects. We are open for consultations for AI health solutions, contact us today.