With the advent of Artificial Intelligence (AI) technology and machine learning, the practice of medicine is evolving rapidly. These AI-based methods, when combined with advances in computer processing, are already increasing the effectiveness and precision of diagnosis and therapy across a wide range of disciplines. Experts believe that AI will eventually replace humans as healthcare professionals. These ideas pose the question of whether AI-based tools will someday replace physicians in certain specialties, or will they just complement physicians’ roles without really replacing them? We need to better comprehend this technology and how it is altering medicine in order to analyze its influence on physicians.
Early Advantages of AI in Medicine:
AI scientists and healthcare experts believe that AI-based solutions will enhance a doctor’s performance in terms of diagnosis, surgery, as well as in prescribing patient-specific drugs, rather than replacing the conventional form of physician-patient interaction. Several elements have that have recently emerged to promote the rate of AI advancements in medicine indicate the same.
Diagnosis:
We know that one of the strengths of AI-based systems is their capacity to acquire and process massive amounts of data and derive conclusions based on their analysis. AI can record and process patient data from multiple sources such as fitness trackers and at-home monitors, allowing doctors to monitor patient’s conditions in ways that would not be possible without AI. Wearable healthcare gadgets, ranging from fitness trackers to portable blood pressure and insulin monitors, will enable remotely managed healthcare solutions through the real-time feeding of data into AI-assisted analytics platforms. This will enable physicians to make better treatment decisions by providing at-home care and in the monitoring of acute diseases.
The likelihood of a patient receiving an improper diagnosis is highly probable as doctors are prone to committing human errors due to varying reasons, while an AI-based system is not. For instance, AI can help doctors estimate the risk of cancer or to analyze various signs or symptoms of a cardiac arrest and stroke. AI and Machine Learning (ML) are critical to enabling precision medicine because they can make sense of vast quantities of clinical, genomic, and imaging data, therefore improving doctor’s efficiency, increasing diagnostic accuracy, and personalizing treatment. This can aid in illness prediction, which is a difficult undertaking for human physicians even in the best of circumstances. Inaccurate mammograms can generate panic and often result in unneeded procedures such as MRIs, ultrasounds, and even invasive biopsies to ensure there is no cancer, which costs time and money and causes physical discomfort.
Prescription:
Imagine personalized healthcare, which tries to customize medical therapy to a patient’s individual characteristics. This has the potential to revolutionize medical treatment by allowing doctors to determine optimal drug doses, identify which genetic mutations cause specific malignancies, and analyze our microbiota. In healthcare, the standard method for determining causation is to perform a randomized controlled experiment. However, such trials are costly, time-consuming, are frequently impractical, and do not adequately reflect diverse types of patients. Whereas AI systems can infer causative linkages from observational data, detailing how multiple elements interact.
Surgery:
Robotic surgery is another area where AI-based solutions are expected to have an influence. Robotic surgery can help surgeons treat patients with more precision, less blood loss, and less discomfort. It decreases the probability of tissue damage since the robotic technology reduces gripping pressures through tactile input, and also allows a surgeon to operate remotely. As a result, AI-enabled robots can aid in reducing surgeon variances that may have an impact on patient recovery. Therefore, in conjunction with augmented reality applications, AI can aid in surgical operations.
Research in Medicinal AI:
AI is being embraced by healthcare executives, however, AI projects are mostly focused on building algorithms that can anticipate a problem, such as cancer, in order to make diagnostics safer, quicker, and cost-effective. Organizations seldom allocate capital to AI systems R&D activities that aim to understand why illnesses develop. Both types of algorithms are required to engage as successfully as possible. By contrast, using causal algorithms, we might discover the underlying causes that cause the disease and utilize this information to design new treatments and to determine who receives that given treatment.
We are more capable of understanding both genetic and clinical causes by creating causal models with data from clinical trials, that can serve as biomarkers for survival, allowing physicians to better tailor the appropriate medication to the right patient. However, it is a challenge for causal AI systems to offer reliable findings that are dependent on accurate statistical data. Countries must now invest in R&D activities that are necessary for the further development of optimal data infrastructures required to sustain these algorithms.
Drug discovery:
The biggest advantage that AI provides in the department of new drug development is the ability to virtually simulate drug tests. It requires a large amount of time, money, and expertise to create numerous permutations of chemical structures and then test their efficacy in fighting a disease. Virtual Simulations make it possible to essentially create an unlimited amount of chemical compounds and test them in virtual chemical reactions before manufacturing them for clinical trials. Thus, the amount of tests required on actual subjects is reduced significantly, and the success rate of experiments increases. This process can potentially save the lives of millions in scenarios where the fast-paced development of vaccines is vital. AI could develop vaccines at an incredibly fast rate, while also predicting and testing potential COVID-19 mutations.
Prosthetics:
The application of AI in prosthetic body parts is one of the most futuristic uses of the technology. The movement and usability offered by today’s prosthetics are extremely limited when compared to their real counterparts. With the help of AI, it is possible to create a prosthetic limb that will be able to perform the complex movements of fingers to perform tasks such as, typing, buttoning a shirt, or even playing a guitar.
However, it will probably take a couple of decades at least for AI technology and bioengineering to reach that level of sophistication. The practice of replacing body parts with artificial ones, for practical as well as aesthetic purposes, has existed for thousands of years. However, there has been minimal progress in terms of their structural design. AI-powered prosthetics could analyze our movements, and predict our intentions without its user having to consciously think about it. This will make mechanical prosthetics just as useful as natural hands, if not more.
Psychology:
One of the more recent areas of interest for AI is psychology, particularly mental health. As AI expands its reach, it is becoming increasingly important for psychologists, therapists, and counsellors to grasp AI’s current capabilities and potential to change mental healthcare. While AI can perform treatment, e-therapy, and evaluations on its own, it can also help human practitioners before, during, and after therapy sessions. Physical examinations assess various indicators such as heart rate and temperature fluctuations in response to specified queries, can also provide a physician with additional information. Collecting data, maintaining records, and initiating automatic follow-up activities would save concerned professionals precious time.
AI technology offers the potential to provide new forms of therapy including virtual and augmented reality assistance through games and other related activities, as well as the capacity to reach and interact with groups that are generally difficult to approach or interact with. However, ethical concerns are unavoidable. Currently, there are no guidelines governing how such technologies are to be built, or how to incorporate them with the activities of health professionals, while also following regulatory requirements. Deciding on the amount of human supervision necessary before, during, and after dealing with clients is another challenge for the government. At the very least, any evaluation or treatment must preserve and safeguard the privacy and autonomy of the patient.
Conclusion:
Radiology, pathology, ophthalmology, and cardiology are some of the specialized fields of medicine, where the incorporation of AI solutions have had a positive impact. AI will result in a diagnosis being more accurate, thorough, and perhaps less expensive over time. It is also capable of accurately predicting illnesses, and in minimizing various unpleasant side-effects or the need for unnecessary testing among patients. This is certainly excellent news for everyone involved, including patients, their families, and healthcare providers. AI approaches and technologies, when combined with human expertise, can enhance care delivery.
However, AI in medicine is still in its early phases, and determining its future is a difficult challenge. Nevertheless, it is safe to conclude that AI certainly has a pivotal role to play in medicine as an assistant. It also allows for the collection and analysis of huge volumes of data, with the potential for increased understanding of diseases and treatment efficacy. If the dangers of unintentional or purposeful abuse, as well as ethical considerations, are effectively handled, AI can facilitate a more optimal method of treating mental and physical disorders or ailments on a global scale.
