ProjectBlog: Between Artificial Intelligence And Healthcare – Leadership Newspaper

Blog: Between Artificial Intelligence And Healthcare – Leadership Newspaper

Artificial Intelligence (AI) 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, analyse, interpret and act on medical data. These abilities of AI will complement human efforts and increase medical success rate and its advantages will enable humans achieve precision in medicine.

It is almost an impossible quest for humans in the medical sector to keep abreast with the increasing inflow of information about health conditions, treatment 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 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.


Digital consultations

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, emphasising machine learning or corpus-based methods and algorithms. Real-time AI conversational analysis together with deep structured learning will solve the problem of answering patient’s 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 personalised medical options, AI can be superior as more data collections are actualised 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 analysing the photos of a patient’s eye, a mobile phone diagnosis of diabetes is possible.

With the existence of an applicable dataset in AI, personalised medication could analyse a person’s gene and chromosome to decide the best treatment, however, such a dataset must be created first.


Healthcare management

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.


In conclusion, the general applications and possible use of AI in healthcare is growing. 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 is very promising.

Source: “artificial intelligence” – Google News

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