AI in Medicine: Applications & Limitations
The future of standard medical treatment, in which a patient first sees a computer before seeing a doctor, could arrive sooner than expected. It appears that thanks to advancements in artificial intelligence (AI), the days of misdiagnosis and treating disease symptoms rather than the fundamental cause may be numbered. Consider how many years of blood pressure readings you have, or how much space you’d have to delete on your laptop to fit a full 3D image of an organ. More uses of artificial intelligence and high-performance data-driven medicine are possible thanks to the growing amount of data collected in clinics and saved in electronic medical records through common testing and medical imaging.
The use of machine learning models to explore medical data and reveal insights to assist improve health outcomes and patient experiences is known as artificial intelligence in medicine. Clinical decision assistance and image analysis are now the most prominent uses of AI in medical contexts. Clinical decision support tools assist practitioners in making therapy, medicine, mental health, and other patient-related decisions by providing quick access to relevant information or research.
The way doctors and researchers approach clinical problem-solving has changed and will continue to change as a result of these applications. While certain algorithms can compete with, and in some cases surpass, doctors in a range of tasks, they have yet to be fully integrated into everyday medical practice. Why? Because, while these algorithms have the potential to have a significant impact on medicine and increase the effectiveness of medical interventions, there are a number of regulatory concerns that must be addressed first. While certain algorithms can compete with, and in some cases exceed, doctors in a range of tasks, they have yet to be fully integrated into daily medical practice. Why? Because, despite the fact that these algorithms have the potential to have a significant impact on medicine and increase the effectiveness of medical interventions, there is a slew of regulatory issues that must be addressed first.
What Makes an Algorithm Intelligent?
Similar to how doctors are educated through years of medical schooling, doing assignments and practical exams, receiving grades, and learning from mistakes, AI algorithms also must learn how to do their jobs. Generally, the jobs AI algorithms can do are tasks that require human intelligence to complete, such as pattern and speech recognition, image analysis, and decision making. However, humans need to explicitly tell the computer exactly what they would look for in the image they give to an algorithm, for example.
AI algorithms are excellent at automating time-consuming jobs, and they can sometimes outperform humans in the tasks for which they’ve been programmed. To create an effective AI algorithm, computer systems must first be given structured data, which means that each data point has a label or annotation that the algorithm can recognize. The performance of the algorithm is assessed to guarantee correctness when it has been exposed to enough sets of data points and their labels, just like tests are given to students. The algorithm can be updated, fed more data, or rolled out based on the testing results to assist the person who built the algorithm in making decisions.
To learn from data, there are a variety of algorithms that can be utilized. The bulk of AI applications in medicine, whether numerical (heart rate or blood pressure) or image-based, require some form of data as input (MRI scans or Images of Biopsy Tissue Samples). The algorithms then use the data to learn and provide a probability or category. For example, the actionable result could be the probability of having an arterial clot given heart rate and blood pressure data, or the labeling of an imaged tissue sample as cancerous or non-cancerous. In medical applications, an algorithm’s performance on a diagnostic task as compared to a physician’s performance to determine its ability and value in the clinic.
Applications of AI in Medicine
1. Disease diagnosis
Years of medical training are required to correctly diagnose diseases. Even yet, diagnostics can be a lengthy and time-consuming process. The demand for expertise in many disciplines considerably outnumbers the available supply. This puts doctors under a lot of pressure, and it frequently causes life-saving patient diagnoses to be delayed. Machine Learning algorithms, particularly Deep Learning algorithms, have lately made significant progress in automatically identifying diseases, lowering the cost, and increasing the accessibility of diagnostics.
2. Faster drug development
Drug development is a famously costly procedure. Machine Learning can improve the efficiency of many of the analytical techniques used in drug development. This might save years of work and hundreds of millions of dollars in investments.
3. Personalized treatment
Diverse patients have different reactions to medications and therapy regimens. As a result, tailored treatment has a huge potential to extend patients’ lives. However, determining which characteristics should influence treatment selection is difficult. Machine Learning can assist determine which variables indicate that a patient will have a certain response to a given treatment by automating this complex statistical work. As a result, the algorithm can forecast a patient’s likely response to a given treatment. The system learns this by comparing and cross-referencing similar patients’ treatments and outcomes. The outcome forecasts that result makes it much easier for clinicians to construct the best treatment plan.
4. Improved Gene Editing
The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) gene-editing technology, specifically the CRISPR-Cas9 gene-editing technique, is a tremendous step forward in our ability to alter DNA inexpensively and precisely, much like a surgeon. In this approach, short guide RNAs (sgRNA) are used to target and modify a specific location on the DNA. The guide RNA, on the other hand, can suit a variety of DNA locations, which can have unintended implications (off-target effects). The careful selection of guide RNA with the fewest harmful side effects is a critical bottleneck in the implementation of CRISPR technology. Machine Learning algorithms have been demonstrated to produce the best results when it comes to anticipating the degree of both guide-target interactions and off-target effects for a specific sgRNA. This could hasten the synthesis of guide RNA for every segment of human DNA.
Limitations of AI in Medicine
1. It needs human surveillance.
2. It may overlook social variables such as class, gender, ethnicity, age grouping, and group identity.
3. It may lead to unemployment.
4. Inaccuracies are still possible.
5. It is susceptible to security risks.
AI is definitely going to revolutionize medicine even though currently there are limitations. The more we digitize and integrate our medical data, the more AI can assist us in identifying useful patterns — patterns that can be used to make correct, cost-effective judgments in complex analytical procedures.
Links Used
2. https://www.datarevenue.com/en-blog/artificial-intelligence-in-medicine
3. https://www.nature.com/articles/s41591-021-01614-0
4. https://drexel.edu/cci/stories/artificial-intelligence-in-medicine-pros-and-cons/
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