Artificial Intelligence (AI) And Its Assistance in Medical Diagnosis
AI in medical diagnosis can help reduce the number of diagnostic errors together with deep learning to gain efficiency in disease detection.
Join the DZone community and get the full member experience.Join For Free
Artificial Intelligence (AI) is a phrase that can be found in practically every business, not just in manufacturing and logistics but also in education, cybersecurity, and many other areas. AI in healthcare was once misunderstood as a competitor to medical experts, but it is today recognized as a second helping hand of professionals that never rests. Artificial Intelligence (AI) in medical diagnostics and healthcare, with its aid and efficiency, gives dependable support to overworked medical practitioners and institutions, reducing workload pressure and increasing practitioner efficiency.
According to research by the Institute of Medicine of the National Academies of Science, Engineering, and Medicine (NASEM), diagnostic errors cause around 10% of patient deaths. They account for almost 17% of hospital problems. However, NASEM said that these challenges are not caused by professional ignorance but rather by a combination of variables such as ineffective communication between patients and organizations, human mistakes, and others.
AI implementation and testing in the medical sector can aid in illness diagnosis and treatment by evaluating massive amounts of treatment and patient data (previous medical data, doctor reports, etc.) and offering relevant assistance and recommendations to clinicians. As a result, the total treatment procedure would be sped up and improved.
Artificial Intelligence (AI) to Improve the Medical Sector
The use of AI in medical diagnostics can assist physicians to enhance medical therapies in a variety of ways. Physical burnout as a result of overwork is a severe problem that many medical professionals experience these days. It reduces medical practitioners’ overall performance, which leads to an increase in diagnostic inaccuracy. The most recent Medscape National Physician Burnout and Suicide Report 2020 data highlighted the risks of putting too much pressure on physicians, particularly those juggling families, retirement plans, and the intricacies of their employment.
Furthermore, AI in medical diagnosis could help reduce the number of diagnostic errors made every year. Making use of the AI ability of deep learning professionals can increase the efficiency of disease detection. A recent study shows that an AI system has achieved the skill of tracking breast cancer similar to an average breast radiologist, demonstrating a 95 percent accuracy rate, published in the journal the National Cancer Institute. In oncology, AI applications are being utilized to identify tumors. Pathologists use machine vision technologies to diagnose diseases in bodily fluids and tissues, and facial recognition helps match phenotypes with specific rare diseases.
Other AI use in healthcare includes the development of new medications and more effective drug targeting to improve efficacy and prevent bad drug effects. Hundreds of startups are actively leveraging AI for drug discovery. For instance, Atomwise (a San Francisco-based startup) has recently joined a partnership knot of 1.5 billion USD with the giant Jiangsu Hansoh Pharmaceutical Group on working to design new cancer drugs.
AI and Internet of Medical Things (IoMT)
The application of artificial intelligence and the Internet of Medical Things (IoMT) in consumer health apps is another possible area where it can flourish its benefits. These solutions put medical IoT devices to gather healthcare records and AI-based medical apps to evaluate the data and provide modifications based on patients’ current lifestyles. The patient-centered approach of medical software developers has brought the in-house trend toward at-home health solutions.
One of the potential implementations under consideration is a voice-based virtual nurse program. The main goal is to improve the hospital room experience and make the process of preparing patients to continue their rehabilitation at home easier. Virtual nurses also help to minimize patient anxiety, improve privacy, keep patients interested, and raise patient satisfaction with medical services.
Challenges With AI in Medical Diagnosis
While implementation of Artificial Intelligence (AI) opens doors of many possibilities but at the same time it raises several challenges for the medical sector:
Safety of Data
The desire for huge datasets encourages developers to acquire such information from a large number of patients. Some patients may be worried that this data collection would infringe on their privacy, and lawsuits have been brought as a result of data sharing between large health institutions and AI startups. AI may potentially compromise patient privacy in another way: deep learning AI can anticipate personal information about patients that was never even revealed to the algorithm. (In fact, this is often the goal of healthcare AI.)
Training AI systems need a massive quantity of data from many sources, including electronic health records (EHRs), medication records, symptom data, and consumer-generated information such as activity trackers or purchase histories. Health data, on the other hand, is usually problematic. Data is frequently spread across several platforms. Aside from the variances stated above, patients routinely change doctors and insurance carriers, resulting in data fragmentation across several systems and formats. This fragmentation increases the risk of inaccuracy, decreases dataset comprehensiveness, and boosts the cost of data acquisition, restricting the sorts of organizations that can construct successful healthcare AI.
A well-known primary concern is that AI systems will be, or can be, inaccurate on occasion, which might have a negative impact on the patient’s life or other healthcare difficulties. If an AI system prescribes the incorrect medication to a patient, fails to discover a tumor on a radiological test, or gives a hospital bed to one patient than another because it mistakenly predicted which patient would benefit more, the patient may experience damage. Despite this, countless injuries occur in the current healthcare system as a result of medical mistakes, even when AI is not involved.
AI flaws can differ from one another due to at least two factors. For starters, patients and physicians may react differently to software-caused injuries than to human-caused ones. Second, suppose an AI system is extensively utilized; a single error can endanger thousands of lives.
In the healthcare business, artificial intelligence has a bright future. Although AI medical diagnosis is not now commonly employed in most clinical settings, experts predict that widespread AI intervention is not far off. And as we progress toward digitalization and integration of medical data, we will see a rise in the use of AI to assist us in making the best and most cost-effective answers for complicated topics.
Published at DZone with permission of Biswarup Bhattacharjee. See the original article here.
Opinions expressed by DZone contributors are their own.