How I Perceive Artificial Intelligence in Healthcare
Despite the benefits of AI in healthcare, we can’t ignore the drawbacks of automation and digitization. Let’s dig deeper into all sides of the dilemma.
The World Medical Innovation Forum held a conference in 2018 focusing on the advancements and opportunities connected to artificial intelligence (AI). During that event, the “Disruptive Dozen,” which is basically a roster of 12 AI breakthrough technologies with the potential to disrupt health care, was proposed.
Minimizing Organizational Load in the Health Care Industry
Artificial intelligence can be used to automate mundane administrative jobs. The US spends more on administrative expenditures than any other modern government. Medical coding and billing are two areas where AI may have a substantial impact. Simplifying coding and billing will lead to fewer errors, less regulatory oversight, and more accurate diagnosis and procedures performed by healthcare professionals. Artificial intelligence (AI) is helping electronic health records (EHR) developers create more user-friendly interfaces and automate clinical duties. Artificial intelligence may be able to handle prescription refills and test results almost entirely in the future. As a result, a clinician’s workload would be significantly reduced. “Fast Healthcare Interoperability Resources” is a new standard for transferring medical and health data. FHIR’s revolutionary approach to health information exchange will usher in a new era of patient-centered care.
Medical Imaging: A New Vision
Radiology tasks, including mammography and ultrasound, are being transformed by artificial intelligence as time progresses. According to the chair of the Department of Pathology at Brigham and Women’s Hospital, breast pathology diagnostics influence a large portion of clinical decision-making. Mammography machines will be transformed from one-size-fits-all gizmos to highly focused tools for assessing breast cancer risk. This would all be result of advances in digital pathology and artificial intelligence in medical imaging.
Facilitating the Route of Diagnosis
It’s now possible to uncover disease using AI in clinical procedures such as imaging scans and pathology detection. In the United States, stroke is major cause of death and disability, hence early detection of such diseases through AI can be helpful for patients as well as doctors. In 2018, the FDA authorized the first completely automated AI diagnostic tool for diabetic retinopathy that doesn’t require expert evaluation.
Bridging Barriers in Mental Health
In the US, roughly one in five persons has a mental illness. Opioid addiction and misuse kill many adults a day. The automated analysis of EEGs and other high-frequency waveforms allows clinicians to spot electrical irregularities known to indicate danger. Deep learning systems using loads of EEG data are helping to identify seizures in critically ill patients.
Extending Access to Healthcare in Underprivileged Areas
Developing nations have a shortage of medical specialists such as ultrasound technicians and radiologists. There are more radiologists in Boston’s six hospitals than in all of West Africa. Because AI can do diagnostic activities often handled by people, it might help ease the present lack of clinical workers. Aside from that, telehealth is a critical tool for providing high-quality medical treatment in locales where it’s otherwise difficult to obtain. Thanks to digital technology, it is possible to reimagine a typical doctor’s appointment as a house call, less the commute. Healthcare providers have traditionally been foremost to develop telehealth platforms because they can afford massive equipment kiosks and advanced digital technologies. As AI solutions turn more cost-efficient, the need for a colossal initial investment in telehealth solutions will continue to dwindle.
Medical Devices and Machines with “Intelligence”
For patient monitoring, smart devices are crucial in the ICU. Using AI to detect deterioration, sepsis, or general complications, can improve outcomes. Such devices will soon be found everywhere. Smart devices currently analyze photos from smartphones and other consumer sources, which will only (indirectly) boost clinical imaging studies in the long run.
The Use of AI-Infused Personal Devices as Health Monitors
More and more health data are being created on the run, ranging from cellphones with step counters to wearables that constantly monitor the wearer’s pulse. Patients’ data from apps and other home monitoring devices may help to better understand individual and community health. Brain-computer-interface (BCI) devices propelled by AI may be able to bring back long lost memories.
Consideration for Patient Privacy
Many say that AI shouldn’t be trusted when it comes to healthcare. With that in mind, there are a few downsides which ought to be mentioned here. Effective risk stratification, predictive analytics, and clinical decision support personnel believe it’s challenging to understand AI due to data quality and integrity problems. Part of the difficult task is integrating data. Mixed data formats, disorganized and unstructured inputs, as well as missing records add to the anomalies at hand. It is more difficult to protect patient confidentiality and security in telemedicine sessions. Protection of patient medical information is ensured by laws such as the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health Act (HITECH). Telehealth practitioners must be well-versed in both state and federal legislation.
Misinterpretations in Data Collection
Another stumbling block to efficient telehealth practices using AI is the precision with which data transfer is carried out. The amount of bandwidth available on the internet affects the validity and reliability of fine motor task measures. If health care practitioners are unaware of the distinctions between technology systems, they could make clinical treatment choices based on erroneous patient charts. Medical pictures and related information are encoded in the Digital Imaging and Communications in Medicine format, recognized worldwide as the gold standard for medical images and associated information.
The Patient-Physician Aura
Unfortunately, many are pessimistic in that AI will oust their negative perceptions towards point-of-care contrivances. Physicians, nurses, and other healthcare workers value patient relationships above all. These AI-influenced interactions are largely based on natural language processing (NLP). In this regard, it’s crucial to remember that not all individuals believe technology deficiencies within the healthcare system are worthwhile to keep around. Lastly, extremely advanced AI technologies are still in their infancy although they are developing further as we speak.
