Artificial intelligence’s role in healthcare is rapidly growing. It can help reduce costs, enhance patient care quality and even create novel medications.
But the impact of AI on healthcare remains poorly understood. This bibliometric study presents an in-depth review of current AI research efforts, their potential impact, and future ramifications.
Diagnosis and treatment of disease
Similar to how predictive mathematical models drive decision-making in finance (such as which stocks to purchase, sell and when), AI is being increasingly utilized to inform clinical decision making, with an aim of both improving patient outcomes and decreasing costs.
AI can ease physicians’ administrative load by automating tedious tasks and minimizing errors during data entry, leaving more time for quality healthcare services delivery.
AI systems can rapidly read MRI scans at much faster rates than their human counterparts and detect abnormalities more accurately, meaning patients can be seen and treated sooner. Additionally, predictive analytics using medical data identifies patterns to predict future events which then helps doctors detect treatment options before they occur, thus increasing treatment outcomes and personalizing care delivery more effectively – an essential step towards unlocking AI’s full potential in healthcare delivery.
Conformance to a medication regimen is central to successful treatment, and AI can play an invaluable role in increasing it. By analyzing patient data and providing valuable insight, one study demonstrated how an AI model predictive of adherence improved clinical outcomes by 30-35% when compared with standard of care treatments.
AI can ease the workload of healthcare professionals. For instance, using it to automate medical documentation could save 34%-55% of physicians’ time – giving them more time for providing quality care services.
AI solutions’ success depends on numerous variables. For instance, medication adherence tools developed with AI could be compromised by individual sociodemographic characteristics and psychosocial profiles as well as cognitive ability of their target patients. Therefore it is imperative that AI solutions be designed with these factors in mind and appropriate training provided to employees to ensure their understanding of this new technology.
Navigating large amounts of information is no simple task for organizations, as often there’s too much scattered data making it hard to locate what you need or form decisions on it.
Artificial intelligence can simplify this process by offering tools that organize and structure data for analysis. Furthermore, robotic process automation provides another useful solution – saving both time and effort while improving insights.
AI can assist data management by minimizing errors made by human employees. For instance, AI can detect logical errors and anomalies in spreadsheets as well as automatically scanning through different formats and linking systems by mapping schemas.
Companies leveraging artificial intelligence (AI) to automate and streamline their data processes will be able to gain competitive advantages from AI-powered automation and streamlining, however technology alone cannot replace good data management practices such as attacking data quality proactively and building organizational structures such as data supply chains.
Integrating AI into healthcare systems effectively will depend on human experts successfully collaborating with this complex technology. This requires having an in-depth knowledge of how the software operates as well as robust security protocols to protect patient data against hacking or breaches, along with transparency that allows medical professionals to comprehend AI decisions made.
AI-powered systems can assist clinicians in improving clinical outcomes by aiding in diagnostics, creating tailored treatment plans, and optimizing chemotherapy dosage. Furthermore, these AI systems may assist emergency departments (EDs) by triaging patients more efficiently and decreasing wait times in emergency departments (EDs).
AI can help organizations identify diseases with high prevalence or geographic distribution and to target resources like vaccines towards them. Furthermore, its analytics for population demographics and disease prevalence allow organizations to target their marketing and outreach campaigns more efficiently. Finally, its algorithms may even be used to monitor patients for any irregularities that might indicate complications or infections in their health conditions that require further medical treatment.