Why It's Worth Investing in Machine Learning for Medical Diagnosis

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Artificial Intelligence (AI) is proving to have a tremendous impact on people in the postmodern world. In fact, the evidence from advances in the field of Artificial Intelligence suggests it has helped improve people's lives

Why It's Worth Investing in Machine Learning for Medical Diagnosis

Machine Learning can be considered as an important subfield of Artificial Intelligence. During the last decade, Machine Learning is widely used in a variety of areas, including the complex healthcare industry. 

The healthcare landscape can be divided into three main categories. These include: 

1. Large Institutions

These could be private hospitals and universities as well as research centers specializing in medical care.

2. Solo Physicians

Physicians who are engaged with private practice belong to this category. They are working hard to resist the trend that focuses on consolidation.

3. Healthcare Professionals

This category includes administrators, physical therapists and nurses who play a major role behind successful practice.

All these practitioners have their own goals and strategies or principles which guide their practice. These goals can further be distinguished into two main categories:

(a) To assist the patients in order to live a healthier life.

(b) To do their work in an efficient way to keep the patients satisfied with treatments.

Challenges for the Healthcare Industry 

Regardless of the background and category of the healthcare industry to which someone belongs, there has been an increasing interest in taking advantage of the potential of Artificial Intelligence such as Machine Learning for medical purposes, like for example medical diagnosis. 

It is not by random that scientists and researchers invest in Machine Learning in this particular area. There are variant reasons which lead to this decision. The effectiveness of this sort of technology holds a prominent place among the reasons for deciding to take advantage of this vast field for medicine. 

Working in contemporary societies means healthcare professionals constantly have to deal with different challenges under stressful environments. Although there is an obvious inbalance between the number of staff available and patients waiting in line to be served, it is not possible to reduce the quality of treatment just because of the high volume of work. In other words, these professionals need to offer the optimum treatment to each and every patient, even under pressure. Physicians who belong to larger groups face perpetual pressure as well because they need to see more patients within the shortest possible time while maximizing their performance. Therefore, time is of essence, as well as accuracy in diagnosis.

On the other hand, Electronic Medical Records (EMRs) which were gradually introduced in most modern healthcare facilities, are constantly being upgraded and changed. As a result, the physicians spend too much time to get the required training in order to effectively use the technology. It is imperative that they follow up any upgrades in order to be able to document all their encounters in electronic medical records.

Cost reduction is another challenge that physicians nowadays are faced with on a daily basis. Both solo practices and large academic institutions have to deal with this challenge. The efficiency of treatments is extremely important on a societal level as well. That’s mainly because it can lead to resource utilization and preventative care.

Investing in Machine Learning

Machine Learning has the potential to provide a convenient solution for all these challenges. Stemming from the proliferation of Artificial Intelligence, Machine Learning has gone through a significant development throughout the last two decades. At the moment, it is powerful enough to make near perfect diagnoses. As a result, practitioners utilizing this technology will be in a position to figure out the best possible treatment for their patients. Additionally, they have the potential to figure out which patients are at a higher risk for poor outcomes and predict re-admissions.


In general, it can be claimed that Machine Learning can enhance the health and well-being of patients while keeping the costs low. The improvements in Machine Learning related to medical diagnosis are taking place at a rapid pace. However, it is still under development and a lot of resources need to be invested to enhance the efficiency of this revolutionary technology.

Machine Learning for medical diagnosis is not something new to the world. It has been there from the early stages of healthcare informatics. During this time, the term “healthcare informatics” was not even used. The Machine Learning algorithms were initially introduced at this time and they were supported by vector machines and Artificial Neural Networks. This area was highly researched at that time and that’s the main reason why you can see it in a large number of publications. Research is still being conducted in this area and it has created plenty of investment opportunities which are now renewing the interest in advancing the field.

Such an example can be found in terms of the accuracy of a diagnosis. It is true that experienced physicians can diagnose the health conditions of patients by having a look at them. However, this method is not 100% effective. That’s where Machine Learning comes into play. The chances of Machine Learning and Artificial Intelligence delivering incorrect results are extremely low. However, it is important to keep in mind that Machine Learning is not advanced enough yet to deliver 100% effective results to healthcare professionals and further research is needed. That’s the main reason why millions of dollars are being invested in this technology.

Research and Practice at a Global Scale

Findings from a recent empirical research paper indicate that Artificial Neural Networks can be used to detect prostate cancer among individuals in an effective manner. The study has a strong interdisciplinary interest in particular among computer scientists and physicians. This is just one study that was conducted in this specific and challenging field of interest; there are several more applications of Artificial Neural Networks which diagnose medical conditions.

Still, despite the different applications of Machine Learning for medical diagnosis, these are not as popular in every corner of the world. The technology is still improving and it appears that a lot of eastern countries invest their money in this technology. However, some of the physicians in Asian countries have not even heard about the concept of Machine Learning. Nevertheless, it is expected that once Machine Learning becomes more widely known there will be high demand because of its real-world applications and practicability.

Conclusions

It becomes obvious that the future of Machine Learning in medical diagnosis looks promising. Healthcare professionals and researchers specialized in different areas of medicine such as cardiovascular diseases and neurological symptoms look to incorporate Machine Learning in their work in order to cope with the aforementioned challenges as well as increase their performance and save more lives. However, it is not possible to advance their science without substantial funding. Therefore, it would be a wise idea for institutions and authorities at large to invest significant proportions of money in Machine Learning since there are numerous benefits as already mentioned in this article.

Overall, it is apparent that professionals and practitioners employing Machine Learning are particularly excited about the meaningful implementation of Artificial Intelligence in the healthcare industry. The most aspiring uses of the technology suggest it has the potential to transform the healthcare industry to a better one which can truly nurture for the longevity of humans. 

The Machine Learning algorithms are constantly being updated in order to achieve better results. Therefore, investing in this technology for medical diagnosis can be considered as an excellent idea for governments and research institutions. This sort of investment decision is a step towards a more fruitful future in terms of healthcare.


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