Significant impacts of artificial intelligence in medicine

Experts believed that applied artificial intelligence in medicine would speed up drug development and clinical trials that bring better personal treatment.

Despite the arising debate of ethical dimensions among experts regarding applying AI in medicines, the future trend of healthcare software development will be expected to nominate artificial intelligence technology. Basically, the term AI has also specified a combination of machine learning, robotics, and natural language processing. Accordingly, apart from AI in medicines, it claims a crucial role in healthcare research, including biomedical and medical education.

By contrast, many experts consider the practical impact of applying AI in medicines related to privacy leaks, regulations, and ethical guidelines. Expressly, since AI is not under the control of any law and policy, it cannot be liable for the flop in processing AI in the healthcare sector. In other words, if doctors are failed in executing their expertise, they have to take legal responsibility for their failures. But AI cannot.

By contrast, many experts consider the practical impact of applying AI in medicines related to privacy leaks, regulations, and ethical guidelines. Expressly, since AI is not under the control of any law and policy, it cannot be liable for the flop in processing AI in the healthcare sector. In other words, if doctors are failed in executing their expertise, they have to take legal responsibility for their failures. But AI cannot.

Fortunately, with the constant innovative technology, the application portion of AI in medicines and healthcare fields are overwhelming the remaining. Accordingly, with the new evolution in healthcare solution development lead by AI, patient preferences could shortly change that instead of meeting doctors, they will first see a computer.

In this article, we provide you insights related to AI in medicine, including available potentials and challenges. By which, you would see an overview of how AI and medicine interact these days.

AI in medicine

Whereas hospitals are overload, and countries are trying to keep social distancing to prevent pandemic spread. The issue of lacking healthcare labors troubles several countries in the wave of infected patients, which rocket in number daily. Those conditions put pressure on the medication sector in the race of vaccine invention. Honestly, we do not have enough people to tackle a large number of works.

COVID19 cause communities concerning remote healthcare or computer as a doctor. This year, you can see the demand for applied AI in medicines dramatically claim a new peak. However, COVID19 is seemingly not the root of advancing AI in the healthcare system. It is only a condition that forces people to admit the inevitable future of ai in medicine. 

Overview of artificial intelligence in medicine  

Experts have shown their optimistic anticipation of artificial intelligence in medicines after COVID. They suggest that the growth rate of this market will significantly increase by roundly 45% in the next five years, reaching US$ 42 billion in size by 2026. The driving indicators of its opportunities are related to complexities in datasets, medication costs, and the need for hyper connection across the world.

The software development segment is believed to takes to the largest share in the healthcare market. Accordingly, healthcare software companies in this decade have a high incentive to focus on AI-driven solutions. Those software solutions profoundly contribute to the operation of healthcare providers in boosting the services and lower cost. Additionally, from the patient’s point of view, they also take advantage of AI medical apps being gradually popular in the market. Hence, they have better soft control of their health.

Related article: Medicine apps as a patients’ assistant

In 2020, the advancement of data-driven medicines is currently helping the doctor in enhancing the accuracy of diagnosis and disease treatment. Electronic medical records accumulated in several clinics and hospitals could become the source of treatment. Besides, AI in medicine also combines with the advancement of spotting patterns, and 3D images gradually transform the industry movements.

Medical artificial intelligence currently has a profound impact on support physicians in diagnosis. Without the support of AI and deep machine learning, doctors might suffer misdiagnoses when considering only human pathology. In the biotech sector, diagnosing complexities defined the time-consuming in the lab for medical training that AI could offer automation in disease detection without the cost of personal training.

When doctors or pharmacists took over four years to get the practicing certificates, AI could be mass-production, tackling the challenges of labor shortages in healthcare sectors. Additionally, technological advancement also reduces the cost of medicine and treatment since it shortens medical research.

Algorithm foundation of artificial intelligence in medicine 

Whereas human brains consider the most complex machines, it is tricky for AI in medicine in simulating doctors or pharmacists. It tries to construct the learning process to do their tasks.

In fact, AI could detect patterns and speech, which will make a decision more accurate, based on image analysis and learning from mistakes.

Basically, the system of AI in medicines leverages images- based algorithm to grow. The software development process needs input raw data (i.e. image) as a data point to be structured in the computer system. Accordingly, the learning authorism would perform an image as a pattern, which is known as models. In practice, an AI-based system requires a large number of datasets to deter bias in the algorithm.

Recently, the algorithm of AI in medicines is commonly based on data in both quantitative data and images. With a sufficient dataset, the AI algorithms automatically generate probability and classification. When making a decision, AI would compare the data input with the models constructed by an algorithm for decision-making.

Recently, the algorithm of AI in medicines is commonly based on data in both quantitative data and images. With a sufficient dataset, the AI algorithms automatically generate probability and classification. When making a decision, AI would compare the data input with the models constructed by an algorithm for decision-making.

Artificial intelligence in medical diagnosis  

In practices, the recent application of AI in medicine is being tested in four key areas: 

• Cancer detection
• Electrocardiograms in accessing cardiac health
• Skin lesions
• Eye retinopathy identification from images.

Fortunately, the initial implications of AI in medicine have been launched broadly into the communities that several healthcare solution apps recently are accessible for download via app stores. Some of them offer valuable support in image diagnosing. For instance, Topol, a medical selfie app, promises to help users detect skin cancer with high accuracy without going to the hospital.

Related article: How IoT solve the challenge of hospital patient tracking systems

Apart from that, artificial intelligence in medical diagnosis is also applied to custom treatments to patients. In fact, each person responds differently to the same drugs and medications. Those responses are believed to depend on some of the type attributes of each human body. The responsibility of AI medical diagnosis is to predict those characteristics. Accordingly, the outcome results are extremely valuable to doctors in preventing drug allergy and anaphylaxis.

Application of artificial intelligence in medicine

Undoubtedly, the practical evidence of artificial intelligence in the medical field is only in the initial stage that needs more advancement to have a footprint in practices. At this time, all we have are in research with several projects on the go.

Deep Learning-based Automatic Detection: a project performed in South Korea focus on chest radiographs. The expected outcome of this project focus on the diagnosis of abnormal cell growth inside the human body. Accordingly, doctors could leverage their input to detect cancers. At the first test, this project performance showed its enormous efficiency that its detection ability outperformed 94% of test participated doctors.

Google AI healthcare: Another application of AI in medicine to identify cancer, Google AI in healthcare has implemented an AI assistant, name LYNA, which helps doctors in doing histology analysis. Results from tests proposed that LYNA have a high capacity to identify undistinguishably suspicious regions from the histology analysis that the human eye got struggling. The accuracy of LYNA was 99%. Accordingly, it would be the expected achievement in cancer diagnosis.

Challenge of regulatory implication applied for AI and medicine

The advantages and potentials of artificial intelligence in medicine are undeniable in that it broadens the horizontal of the healthcare sectors. However, the improvement of regulation seemingly does not catch-up with technological advancement.

Currently, it has not any official guidelines for approving the algorithm of AI in medicines released in any country. It is only in processing, notwithstanding some governments start considers authorizing some assistive AI applications.

Related article: 7 upcoming tech trends in 2020 will shape your business

Additionally, the contests against using AI in medicines still exit that proponent expresses their suspect regarding its practicality. Whereas the human brain’s ability is unlimited, and doctors steadily update their approach and expertise through the treatment process, computers are constraints in algorithms constructed from the development process. Hence, authorities are currently struggling with changing the regulation system to follow AI innovation.

Final thought: Despite challenges, AI in medicines is the promised land to tackle healthcare system challenges. The development of AI will benefit not only medication services but also software development companies. Accordingly, it brings value to the sustainable development of human beings.