Artificial Intelligence in Health Care
Author – Ritu Yadav
MMS-65
Literature Review
News and Analysis of the Global Innovation Scene.
The article talks about how artificial intelligence (AI) is becoming more and more prevalent in healthcare, especially in light of the COVID-19 epidemic. It looks at several ways AI might be used in healthcare, including automating administrative duties, researching treatments, prioritising patients, and forecasting the spread of diseases. The paper focuses on the potential advantages of AI in healthcare, including better diagnostics, quicker medication development, superior imaging analysis, and more effective administrative procedures. It also draws attention to issues that prevent wider adoption, including the confidentiality of medical data, the openness of AI results, liability allocation, and regulation. In order to successfully use AI in healthcare, it is necessary to give these issues significant thought.
The Changing Nature of Work
The article explores the use of artificial intelligence (AI) in healthcare while discussing how the nature of labour is evolving. It highlights how quickly both manual and intellectual labour are being transformed by technological breakthroughs, especially AI. Robotic surgical tools and illness diagnosis are only two examples of how AI is being used in healthcare. Technologies like CRISPR/Cas9 have ramifications for workplace health and safety because they enable synthetic biology and genetic engineering. The paper contends that although technology and AI can enhance human welfare, their effects on society, especially the healthcare industry, must be recognised and carefully handled.
How investment in AI for healthcare organizations has changed due to the pandemic.
The COVID-19 pandemic has caused a dramatic shift in the amount invested in AI by healthcare organisations. The healthcare industry is starting to adopt AI more quickly and recognise its value. With CFOs realising the significance of swift digital transformation for organisational survival, many healthcare leaders now see AI as a crucial innovation driver. In order to streamline administrative, clinical, financial, and operational operations, healthcare providers are rapidly investing in automation, including robotic process automation and machine intelligence. The epidemic has brought to light the necessity for artificial intelligence to anticipate and plan for uncertainty. Health systems are analysing their AI investment choices, creating centres of excellence, and concentrating on AI prospects that offer radically improved efficiencies and fit with their long-term plans.
Most healthcare organizations want to augment current EHR workflows and would consider flexible outsourcing contracts.
To address issues with revenue cycle management, healthcare organisations are increasingly using automation, artificial intelligence, and flexible outsourcing contracts. According to a poll, 74% of organisations are now augmenting EHR workflows with new tools or are considering doing so, and nearly 84% believe that flexible outsourced contracts are beneficial. This tendency is being driven by dissatisfaction with revenue cycle reporting and EHR functionality. Using data-driven account prioritisation and analytics powered by technology to prioritise work activities helps to maximise productivity. Organisations that are proactive are looking into workforce and technological options to support a high-performing revenue cycle. Healthcare providers may assure effective revenue cycle management by tracking yield performance in real-time and utilising flexible workforce solutions.
Exploring behavioural intentions toward smart healthcare services among medical practitioners: a technology transfer perspective.
This study examined the variables affecting doctors’ and non-clinicians’ intentions to embrace smart healthcare services. The perceived usefulness, attitude, and experience with mobile health (mHealth) technology strongly influenced adoption intentions for both groups, according to survey data from 484 doctors in Anhui, China. Clinicians’ behavioural intentions were influenced by subjective norms, whereas non-clinicians’ attitudes were influenced by perceived danger. The use of mHealth had the biggest favourable impact on doctors’ intention to embrace the technology among the characteristics examined, improving perceived utility and usability and lowering perceived danger. These results provide useful information for product development and marketing plans intended to increase consumer acceptance and use of smart healthcare services.
Demystifying AI in Healthcare: Historical Perspectives and Current considerations.
The application of artificial intelligence (AI) in healthcare is discussed in light of the growing concerns and misunderstandings in this area. The article gives a historical review of machine learning and clarifies important AI terminology because they see healthcare as the next AI frontier. They provide case studies that show how AI is being used to improve healthcare by lowering errors, automating computations, increasing results, expediting contract negotiations, and giving patients cost-effective experiences. The limitations of AI in healthcare are examined, and suggestions for governance and control are made. The article’s overall goals include addressing problems, offering insights, and providing advice for the successful application of AI in the healthcare industry.
Resistance to Medical Artificial Intelligence.
The study looks at how open customers are to using AI in healthcare and discovers that they are cautious about adopting these services. They are less likely to use healthcare services provided by artificial intelligence (AI), have lower price expectations, and are less sensitive to changes in AI providers’ performance. Consumers also believe that automated healthcare is less effective than healthcare provided by humans. This opposition to medical AI is a result of “uniqueness neglect,” where customers feel that AI providers are unable to take into account their unique qualities and situations. People who consider themselves to be more special tend to be more resistant. Consumer resistance is mediated by uniqueness neglect; however, interventions like personalising AI, caring for others, or portraying AI as a helpful tool rather than a replacement for all existing technologies can help.
Federated Learning for Smart Healthcare: A Survey.
The article looks at how communication technologies and the Internet of Medical Things (IOMT) have changed the way artificial intelligence (AI) is used in smart healthcare. Federated Learning (FL), a distributed collaborative AI paradigm appropriate for the healthcare industry, is introduced. The essay offers a thorough analysis of FL in smart healthcare, encompassing current developments, drivers, needs, FL designs, applications, project analyses, lessons learned, and research difficulties. Overall, it is a useful tool for comprehending and advancing the use of FL in intelligent healthcare.
Artificial Intelligence in Health Care? Evidence from Online Job Postings.
In summary, the study finds that artificial intelligence (AI) adoption is currently low in the healthcare industry, particularly in hospitals. Less than 3% of the 4,556 hospitals in the research that had jobs that required AI skills between 2015 and 2018 were represented in the analysis of data from online job postings. According to the report, AI abilities were necessary for about 1 in every 250 hospital employees at that time. Analyses using statistics are constrained by the low adoption rates. The data did reveal that hospitals with integrated salary models, larger hospitals, and counties with greater populations were more likely to embrace AI. According to the findings, there are a lot of obstacles standing in the way of the healthcare sector adopting AI widely.
Artificial Intelligence as a Growth Engine for Health Care Startups: Emerging Business Models.
The article’s conclusion is that incorporating artificial intelligence (AI) into healthcare has the potential to change the healthcare landscape by reducing information asymmetry between healthcare providers, payers, and patients. Although technology suppliers play a vital role, the real strength of AI lies in opening doors for entrepreneurs to target certain issues and industry verticals. AI can transform how clinical and administrative staff access information and allocate resources, empowering patients to take more charge of their own health.The study emphasises how important business model design is for successfully commercialising relevant technology, especially in data-driven healthcare. In order to address security and privacy concerns related to handling sensitive data, both new entrants and established businesses are constantly developing new solutions.
Conclusion
In conclusion, the provided references offer a comprehensive overview of the use of artificial intelligence (AI) in healthcare. The application of artificial intelligence (AI) in healthcare is discussed in light of the growing concerns and misunderstandings in this area. The article gives a historical review of machine learning and clarifies important AI terminology because they see healthcare as the next AI frontier. They provide case studies that show how AI is being used to improve healthcare by lowering errors, automating computations, increasing results, expediting contract negotiations, and giving patients cost-effective experiences. The limitations of AI in healthcare are examined, and suggestions for governance and control are made. The article’s overall goals include addressing problems, offering insights, and providing advice for the successful application of AI in the healthcare industry.
References
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