While artificial intelligence (AI) is not new, most healthcare organizations are still in the early stages of adopting it.
Nonetheless, AI is gaining a foothold in this area because it can be applied to extract new insights from vast amounts of data – a more than welcome support for staff who are always overworked.
According to forecasts, the amount of data available will increase by 36 percent annually by 2025. Yet, only a fraction of it is used to enhance the quality and efficiency of patient care.
This tremendous increase in volume and diversity challenges the speed of data collection and this is where AI can help.
In this post, we talk about AI in healthcare, its benefits as well as challenges and potential risks.
Typical Applications of AI in Healthcare
AI applications are increasingly appearing in various areas of healthcare. These primarily include imaging and radiology, but AI is already present in robotics for surgery, nursing, rehab, and orthopedics.
Other applications include:
- analysis of ECGs
- skin and eye images
- personalization of treatments
- disease diagnosis
- drug development
- software in digital health applications
- computer-based augmentations of reality perception (augmented and virtual reality – AR and VR)
Artificial intelligence is also a popular tool in the development of drugs. A more efficient approach to analytical processes can save a lot of time.
Personalizing treatments can be life-saving. Designing the right treatment plan for a patient requires a lot of statistical work. The latter can also be automated using artificial intelligence.
Also, AI is already helping with gene editing. To edit the DNA as desired, the right choice of so-called guide RNAs is crucial, and AI minimizes the risk of unwanted side effects.
Benefits of AI in Medicine
In medicine, in particular, the fields of application for AI are extensive. For example, with the aid of AI, patient care can be made much more efficient, and medical staff can be relieved. Among other things, this is achieved by automated medical devices and system controls.
Some of the key benefits also include:
- Improved Accuracy and Precision
AI algorithms can analyze large amounts of data and make predictions with a high degree of accuracy. This can help doctors make more accurate diagnoses and provide more effective treatments.
For example, in analyzing and reviewing mammograms and radiology images AI can accelerate the process up to 30 times with 99% accuracy. This is also concurred in the 2017 Stanford University published study where they described the successful use of AI algorithms in skin cancer detection against the diagnosis of 21 dermatologists.
- Faster and More Efficient Decision-Making
AI can help doctors make decisions more quickly by analyzing data and providing recommendations in real-time. This can be especially helpful in emergency situations where quick decision-making is critical.
- Increased Availability of Care
AI-powered systems can help provide care to more people, including those in remote or underserved areas. For example, telemedicine applications can use AI to diagnose and treat patients remotely.
- Reduced costs
By automating certain tasks with AI, healthcare facilities are able to provide faster, more efficient care and reduce healthcare costs.
- Improved Patient Outcomes
By providing more accurate and personalized care, AI has the potential to improve patient outcomes. For example, AI-powered systems can help doctors identify early warning signs of diseases and intervene before they become more serious.
Challenges of AI in Healthcare
The global market for algorithm-based healthcare solutions is expected to grow from $6.7 billion in 2020 to $120.8 billion in 2028, however, some challenges still exist. For example, AI in healthcare must not only adhere to ethical standards and protect sensitive patient data, but it must improve patient outcomes to be adopted as well.
- Complexity of Data Transfer
As with many things, AI also has factors that slow its development. For instance, training data-driven AIs, such as those used in image processing in radiology, requires many excellent-quality data sets.
However, getting hold of these sets is still a significant challenge in some cases. It is also complex to transfer the many years of experience of medical professionals into an AI.
Furthermore, during training and in the actual operation of an AI, one should ensure that the AI does not unlearn what it learned at the beginning after a certain period.
For AI to be used most effectively in healthcare, deep-learning models and results should be easily integrated into the workflows of all healthcare professionals. In addition, medical data must be compatible across different platforms to improve interoperability and access.
- Potential To Misinterpret the Artifacts
The question of responsibility for AI decisions in a medical context arises. As an illustration, it is possible that carcinomas and metastases on CT images will be detected better, faster, and more reliably by AI systems, but who is liable if artifacts are misinterpreted and have severe consequences for the ill person?
Medical professionals cannot review each decision made by AI. Still, the physician or health care professional must ultimately be medically and legally free to make certain life-critical decisions.
Since AI may generate its own decisions, this responsibility and liability can be challenging to answer but should be clarified lawfully and transparently.
In addition to the many benefits of AI in medicine, it also carries certain risks.
Perhaps the biggest concern is that data and diagnostics could be inaccurate or wrong. If data is faulty or insufficient, it may give incorrect results that harm the patient’s treatment.
Although AI can make excellent and precise predictions, it is often impossible to understand how it arrived at these results. In addition, medical data is sensitive data that must be protected. However, critical ethical issues, including data protection, must be clarified.
Experts repeatedly emphasize that machine learning systems, i.e., systems that, as part of artificial intelligence, can learn automatically from experience (data) and improve themselves without being explicitly programmed, but this should not be blindly trusted.
The decision-making authority should be at the hands of a the doctor, even with the help of AI. While the algorithm only assesses a section, the doctor can see the patient as a whole.
The use of artificial intelligence also raises ethical questions. Ethical guidelines are needed to ensure that these systems are safe to use.
Some researchers argue that AI authors can be only partially free of bias, which poses a serious problem because AI authors are often used in sensitive contexts such as healthcare, law enforcement, and education.
Furthermore, critics see the potential for new power asymmetries in which AI systems could one day be superior to humans. Should an AI be allowed to decide about life and death? Questions like these must be considered when using AI in the future.
The Future of AI in Healthcare
The most significant assurance of AI in healthcare comes from changing clinical workflows. AI can contribute by either augmenting or automating the work of staff and clinicians.
As a result, many day-to-day tasks will become completely automated. Furthermore, with AI help, health professionals can perform better and upgrade patient outcomes.
So, how is AI revolutionizing medicine even further?
A study by the University of Stanford predicts that electronic devices and apps will monitor our state of health as early as 2030. In surgery or in-patient care, robots are to replace working people and improve not only the healthcare system but also life expectancy. Whether that will be real or is a dream of the future remains to be seen.
AI in Healthcare: Key Takeaways
The potential of artificial intelligence in healthcare is very high as it offers many benefits, including:
- Improved accuracy and precision
- Faster and more efficient decision-making
- Increased availability of care
- Reduced healthcare costs
- Improved patient outcomes
However, there are still some difficulties and risks associated with the help of AI in healthcare, including the potential for bias in algorithms, the complexity of data transfer, and ethical use.
While AI can revolutionize healthcare, it also requires careful consideration and planning to overcome the challenges and ensure successful implementation.
It is essential to carefully consider all the aspects of it to ensure that it is used in a way that maximizes its potential and minimizes any negative impacts for a better quality of life.