Future medical procedures could arrive soon: Misdiagnosis and treating disease symptoms rather than their cause may become a thing of the past as a result of AI’s recent development and advancement. Think about how much memory you would need to free up on your computer to accommodate a complete 3D image of an organ or the years’ worth of blood pressure records you have collected. A high-performing electronic-driven medical era is possible because to the vast amounts of data collected and saved in digital medical records through medical pictures and general testing. These AI applications are influencing how researchers and medical professionals approach fixing medical problems.
Currently, clinical decision assistance and image analysis are the two most frequent uses of AI in medical contexts. Clinical decision support systems give healthcare professionals instant access to data or studies that are pertinent to their patients, assisting them in making decisions about treatments, drugs, mental health, and other patient needs. To analyse CT scans, x-rays, MRI’s, and other pictures for lesions or other discoveries that a human radiologist would overlook, AI technologies are being employed in the field of medical imaging.
Many healthcare organizations throughout the world have been field-testing new AI-supported technologies, such as algorithms meant to support patient monitoring and AI-powered tools to screen COVID-19 patients, as a result of the difficulties that the COVID-19 pandemic brought for many health systems.
AI applications in medicine
AI has a variety of potential benefits for the practice of medicine, including accelerating research or assisting clinicians in making wiser judgments. Here are some examples of possible applications for AI:
1.AI for identifying and diagnosing diseases
AI, unlike humans, doesn’t require sleep. To monitor the vital signs of patients getting critical care and notify clinicians if particular risk indicators grow, machine learning models might be utilized. Heart monitors and other medical devices can monitor vital signs, but AI can gather the data from those devices and search for more complex illnesses like sepsis.
According to reports, the interpretation of pulmonary function tests represents a viable area for the creation of AI applications in the field of pulmonary medicine. According to a recent study on interpreting the results of pulmonary function tests, AI-based software offers more accurate interpretation and acts as a decision support tool. In one of the several criticisms of the study, it was said that the rate of accurate diagnosis among the pulmonologists taking part in the trial was significantly lower than the national average.
Diabetes patients with continuous glucose monitoring can see interstitial glucose measurements in real-time and receive data on the direction and rate of change of blood glucose levels The Guardian smartphone-connected glucose monitoring solution by Medtronic has obtained FDA approval. In order to help its customers better prevent hypoglycemia episodes based on repeated measurement, the company joined with Watson (AI developed by IBM) for their Sugar.IQ system in 2018. Continuous blood glucose monitoring can help patients improve their blood glucose control and lessen the stigma associated with hypoglycemic episodes, but a study on patients’ experiences with glucose monitoring found that while participants expressed confidence in the notifications, they also admitted to feeling personally unsuccessful in controlling their blood glucose levels.
Clinical nephrology has used artificial intelligence in a number of settings. For example, it has been shown to be helpful for determining the risk for developing progressive IgA nephropathy and for predicting the reduction in glomerular filtration rate in patients with poly cystic kidney disease (30). A recent analysis, however, shows how the sample size required for inference currently limits research.
5.Cancer Computational Diagnosis in Histopathology
Paige.ai’s AI-based algorithm has been given breakthrough designation by the FDA for its ability to accurately diagnose cancer using computational histopathology, giving pathologists more time to concentrate on crucial slides.
6. Medical Imaging and AI-Based Technology Validation
A long-awaited meta-analysis compared the abilities of deep learning algorithms and radiologists in the field of imaging-based diagnosis. Although deep learning appears to be as effective as radiologists for diagnosis, the authors noted that 99% of studies were found to not have a reliable design; furthermore, only 1% of the papers that were reviewed validated their results by having algorithms diagnose medical imaging from other source populations. These findings confirm the requirement for thorough clinical studies to validate AI-based technology.
Advantages of artificial intelligence in medicine
1.Informed patient care
When clinician workflows incorporate medical AI, it can provide important context for decision-making. By delivering doctors useful search results with evidence-based insights on treatments and procedures while the patient is still in the room with them, a trained machine learning system can reduce the amount of time they need to conduct research.
Some data suggests that AI may help to increase patient safety. AI-powered decision support tools can aid in improving error detection and drug management, according to a recent systemic assessment of 53 peer-reviewed research looking at the effect of AI on patient safety.
3.Reducing the costs of care
There are many potential ways AI could save expenses throughout the healthcare sector. The most potential opportunities include lowering pharmaceutical errors, individualized virtual health support, preventing fraud, and assisting with more effective administrative and clinical procedures.
4.Giving context-relevant information
Deep learning has the key benefit of allowing AI algorithms to discern between various forms of information by using context. A well-trained AI algorithm can use natural language processing to determine which pharmaceuticals belong in the patient’s medical history, for instance, if a clinical note includes a list of a patient’s current prescriptions as well as a new medication their provider suggests.
5.Increasing patient-doctor interaction
Many patients have inquiries after regular business hours. Through chatbots that can respond to simple questions and provide patients with information while their provider’s office is closed, AI can assist provide round-the-clock support. AI may also be used to priorities queries and mark data for scrutiny, which could assist clinicians be informed of health changes that require further attention.
Future Directions and Challenges of Artificial Intelligence in Medicine
1.Will Artificial Intelligence Replace Doctors?
Artificial intelligence won’t likely replace doctors because there are already smart medical devices that can help them better manage their patients. However, as though the two counterparts were in competition, comparisons between medical professionals and artificial intelligence solutions routinely take place. Future research should compare doctors who use artificial intelligence applications to doctors who don’t, expanding those comparisons to translational clinical trials; only then will artificial intelligence be accepted as a medical complement. Although a significant overhaul of medical education is required to give future leaders the competencies to do so, healthcare professionals are currently in a unique position to welcome the digital transformation and be the key drivers of change.
2.The Demand for Education of Augmented Physicians
In response to the requirement to prepare future medical leaders for the difficulties posed by artificial intelligence in medicine, several universities have begun to develop new medical curricula, including a doctor-engineering programme.
In such curriculum, the hard sciences (such as physics and mathematics) are approached more rigorously, and computer sciences, coding, algorithmics, and mechatronic engineering are added. These “augmented doctors” would draw on both their clinical knowledge and their digital competence to address the challenges of contemporary medicine, assist in developing the digital strategies for healthcare organizations, oversee the digital transition, and instruct patients and colleagues.
These specialists might serve as a safety net for all procedures, including the use of AI in healthcare, as well as a catalyst for innovation and research, which would be advantageous to society and healthcare institutions. To enable retraining in this expanding field, ongoing educational programmes relating digital medicine that are targeted at graduated physicians are therefore needed in addition to basic medical education. These professionals are tasked with carrying out the duties of the Chief Medical Information Officer (CMIO) in the majority of cutting-edge hospitals around the globe.
3.AI-Based Technology Validation: A Crisis of Replication?
The clinical validation of the fundamental ideas and recently created tools will be one of the main problems of AI’s application in medicine in the upcoming years. Although numerous research have already demonstrated the potential of AI based on encouraging outcomes, validating these claims is expected to be complicated by a number of well-known and widely documented limitations of AI studies. We will discuss three of these restrictions in this article and offer potential solutions.
4.Ethical Implications of Continuous Monitoring
One of the most promising markets of the twenty-first century is medical technology, which is predicted to have a market value of close to $1 trillion in 2019. The selling of medical equipment (such heart monitoring devices) to a younger population, which is not the primary target consumer profile (since health issues like atrial fibrillation are less likely to occur), is responsible for a growing portion of the revenue. This phenomena has led to the Internet of Things (IoT) redefining what a healthy person is as a combination of the quantified self (personal indicators programmed in the smartphone or wearable) and a variety of lifestyle criteria offered by wearable (activity monitoring, weight control, etc.).
A potential area of research and development is the application of artificial intelligence in clinical practice, which is developing quickly alongside other cutting-edge disciplines like precision medicine, genomics, and teleconsultation. Health policy should now be concentrated on addressing the ethical and financial challenges related with this pillar of the evolution of medicine, while scientific research should continue to be rigorous and transparent in creating new solutions to improve contemporary healthcare.