The Adoption of AI and Machine Learning in Healthcare: What is the Right Way to Proceed?

The amount of data generated by the healthcare industry grows, increasing the need to use Artificial Intelligence (AI). Let’s find out how AI-powered technologies are being adopted in healthcare, given all the restrictions and benefits AI brings to the field.

The potential of Healthcare with Artificial Intelligence

AI has a great potential to improve the quality of healthcare as it can help spot diseases faster, and automate many processes. Healthcare providers can benefit from it, since AI allows them to focus on a patient-centered approach that increases staff retention.

Examples of AI-based solutions in Healthcare

Through predictive analytics, AI can be rapid, precise, and in the long term, cost-saving way to augment experience and intuition. As the spectrum of AI includes various technologies, there are many ways how AI can be applied to healthcare. Machine Learning is mostly used in precision medicine to make personalised treatment plans. Natural Language Processing helps analyze patient records and creates medical reports based on data sets. Experts in healthcare utilize Rule-based systems to create domains of ‘if-then’ rules to support medical decision-making. Physical robots improve the precision of incisions, making operations less invasive. Robotic process automation is applied by healthcare organizations to perform administrative tasks.

Top AI applications in Healthcare

There are numerous ways of implementing AI to the healthcare field. In this article, we will describe ways of applying AI technologies that play the most important role in the healthcare industry.

Diagnosis and Treatment

AI-based applications can help make the process of disease detection more precise. Specifically-trained machines can scan big chunks of data and detect abnormalities and first signs of a disease. For instance, tech companies work on creating an application that takes daily measurements of a human’s heart rate and warns users of possible health risks.

AI-assisted surgeries

With an AI-based approach, modern surgeries can become more effective in terms of decision-making, patient-oriented treatment, and situation analysis. Robots are proven to be useful at analyzing previous operation history of the patient and making quick suggestions about the best choice of instrument and surgical techniques.

Health records improvement

With the help of blockchain technologies, a patient’s personal data can be securely stored. This means that the patients can contact the healthcare provider to solve any type of problems, keeping in mind that information about their diseases is private.

Personalizing treatment

Because every patient wants a better remedy, more attention, and effective medical treatment, personalized care is a critical issue in the healthcare industry. Given their ability to self-train, AI algorithms can bring value to the issue of personalized care in healthcare.

Patient engagement

Even though doctors create personalized healthcare plans, this has little effect because a patient fails to follow basic steps like taking medications or scheduling a check-up appointment. Increasing patient engagement will not only improve patient experience but will also generate financial inflows. With pop-up messages and goal-oriented content, AI algorithms can prompt patients to take specific actions.

Administrative issues

AI can save time by dehumanizing administrative processes. The Robotic Process Automation technology can process claims, keep clinical records, and manage the revenue cycle. Such a technological approach to administrative tasks can save up to one fourth of time spent by a medical worker performing those tasks.

The biggest challenges for AI and ML in Healthcare

Unemployment among the industry workforce

Since AI-based technology can automate various processes, it can create a serious threat to employment in the healthcare segment. Some businesses may simply no longer need workers because AI can be trained to perform those tasks.

Ethical issues and regulations

When machine learning systems are examining someone’s health to come up with a diagnosis, transparency can be a serious question to consider. If someone were told that they are diagnosed with cancer, they would want to know the logic behind the deep learning algorithms. Since empathy is not embedded in AI algorithms, receiving important information about diseases can be depressing for patients.

Bias and inequality

AI algorithms can be biased by race, gender and age. When training an algorithm to determine the probability of getting a disease, AI analyzes tons of data records. If the records do not cover a distinct target group, then all further estimates will be inaccurate.

Data protection and privacy concerns

AI-based apps store a lot of patients’ personal data, including details on diseases, allergies, pathologies, and medication use. Leaking this information to third parties can destroy user loyalty and cause serious career or relationship problems for the patient. There is no success for AI in healthcare if patients do not feel safe about their personal information.

Strict quality control issues

AI may make mistakes that could jeopardise a patient’s health. If an AI system offers the wrong medicine to a patient or fails to detect a disease, such errors can have serious consequences.

Errors and misdiagnoses

There are certain aspects of human health that require a detailed human analysis. For instance, AI can enhance radiological diagnosis but there is no algorithm that can perform complex series of tasks and examinations like real doctors do. AI may misdiagnose because of rounding percentage errors and fail to take other medical factors into account.

How to deal with the possible risks of AI and ML in Healthcare

Start with a specific use case

Along with ethical issues, it is important to spread information about AI’s potential in healthcare. Be it doctors or patients, people should be informed about recent cases where AI algorithms boosted the efficiency of healthcare delivery. Illustrating successful examples of applying AI technology in healthcare can increase public confidence in it.

Make safety, transparency and ethics a top priority

In healthcare, the biggest question is whether AI technology will become widely adopted. For this to happen, patient data should be stored in a secure way and AI algorithms should be confidential.

Educate machines and healthcare staff

To successfully implement AI in healthcare, this technology has to be authorized by healthcare representatives, adjusted to the standards of other equipment, and updated with new information. Physicians must be trained to learn how to benefit from using AI technology.

Control AI system quality

For AI diagnostic systems to work with high accuracy, algorithms should be regularly checked and upgraded. By providing maintenance by well-trained developers, the quality of AI systems in healthcare can be greatly improved.

Benefits of properly used Artificial Intelligence in Healthcare

Early diagnosis

Machine learning has made precision medicine possible. Using its self-learning algorithms, AI can help doctors show the best results in diagnosis and treatment recommendations.

Improved accessibility

In developing countries, the lack of medical services leads to low life expectancy. AI can take over diagnostic responsibilities to reduce the shortage of healthcare services. For instance, ML-based image diagnostics can analyze X-rays, MRI, and CT scans. This technology can be applied in place of doctors, which will make healthcare cheaper and thus more accessible.

Reduced costs

Applying AI can reduce the cost of diagnosis and treatment. For example, ML algorithms are 15 to 33 percent more effective at predicting the cost of lung cancer treatment than traditional methods, which can help allocate financial resources efficiently.

Surgical care free from human error

AI-equipped surgical systems can perform the smallest movements with high precision. This not only decreases the wait time for treatment, but also reduces the danger of blood loss, and injuries because of human factors.

Final Thoughts

The amount of patient data continues to grow both inside and outside hospitals. Given the financial challenges, operational inefficiencies, and shortage of skilled healthcare professionals, healthcare organizations need to adopt modern technology. AI can provide digital solutions to the aforementioned challenges because it has limitless potential to improve healthcare quality by accurately evaluating and interpreting vast amounts of data.