The Impact of AI in Healthcare and Smart Hospitals

Many AI systems are initially designed to solve a problem at one healthcare system based on the patient population specific to that location and context. Scale up of AI systems requires special attention to deployment modalities, model updates, the regulatory system, variation between systems and reimbursement environment. Here, we summarise recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective AI systems and discuss the possible future direction of AI augmented healthcare systems.

Virtual reality can help current and future surgeons enhance their surgical abilities prior to an actual operation. Timings are illustrative to widescale adoption of the proposed innovation taking into account challenges / regulatory environment / use at scale. Indexed databases, including PubMed/Medline (National Library of Medicine), Scopus, and EMBASE, were independently searched with notime restrictions, but the searches were limited to the English language. EHRs haven’t completely replaced paper yet, and even though their use is pervasive, receptionists, medical assistants and doctors must do a lot of manual entry. So, instead of typing information into the system, the user can simply speak the information they want recorded in the EHR.

Success Factors of Artificial Intelligence Implementation in Healthcare

He has over 8 years of experience in market research, competitive intelligence, financial analysis, and research report writing. Three studies that reported facilitators did not report any barriers [38,39,47], and two studies that reported barriers did not report any facilitators [33,35]. Five co-authors (LMR, TOS, MATH, MT, PDN) read the full papers, extracting any section that pointed to a possible barrier or facilitator.

Some of the more common approaches involve drug candidate identification via molecular docking, for prediction and preselection of interesting drug–target interactions. Machine learning opportunities within the small molecule drug discovery and development process. It is believed that within the next decade a large part of the global population will be offered full genome sequencing either at birth or in adult life.

Data analysis

How we tackle issues in implementation in the next few years will likely have far-reaching impacts for the future practice of medicine. In the scope of this study, we refer to AI as systems that are used to solve healthcare problems of interest and are powered by ML. Witten et al. [18] define ML as ”a family of statistical and mathematical modeling techniques that use a variety of approaches to automatically learn and improve the prediction of a target state, without explicit programming”. This definition precludes most expert systems and other basic knowledge-based AI systems that use simple rule-based processes or Boolean rules. First, solutions are likely to address the low-hanging fruit of routine, repetitive and largely administrative tasks, which absorb significant time of doctors and nurses, optimizing healthcare operations and increasing adoption. In this first phase, we would also include AI applications based on imaging, which are already in use in specialties such as radiology, pathology, and ophthalmology.

Integration of big data concerning health and disease will provide unprecedented opportunities in the management of healthcare information at the interface of patients, physicians, hospitals and healthcare authorities, and regulatory bodies (Fig. 2). The development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation machine learning implementation in business has not yet become a reality. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China. This study exemplifies a theory-based approach to synthesizing determinants of AI implementation success and formalizes known gaps and biases related to how AI implementations are reported.

How is AI in healthcare perceived?

These companies have shown that their AI can beat humans in selected tasks and activities including chess, Go, and other games. Both IBM Watson and Google’s Deep Mind are currently being used for many healthcare-related applications. IBM Watson is being used to investigate for diabetes management, advanced cancer care and modeling, and drug discovery, but has yet to show clinical value to the patients. Deep Mind is also being looked at for applications including mobile medical assistant, diagnostics based on medical imaging, and prediction of patient deterioration [6], [7]. There was also consensus among the healthcare leaders that the county council should collaborate with companies in AI systems implementation and should not handle such processes on their own. An eco-system of actors working in AI systems implementation is required, who have shared goals for the joint work.

In January 2021, 61% of those in the C-suite said their organization planned to deploy AI or machine learning tech in the coming year, per the BDO USA survey. That is significantly higher than the 38% who said their organization was currently deploying the technology. Leaders’ views the implementation of AI systems would require the involvement and collaboration of several departments in the county council across organizational boundaries, and with external actors.

Electonic Health Records

Existing values and understanding of care can become barriers to trust in AI in relation to implementation in healthcare if there is a lack of coherence. There is thus a need to understand the context in relation to implementation (59) to be able to align AI to existing values (38, 57). Differences in values must be considered for trust to be present when implementing AI in healthcare.

• The more patients proactively participate in their own well-being and care, the better the outcomes – utilisation, financial outcomes and member experience.
• The development of early diagnostic tools is an ongoing challenge due to the complexity of the various disease mechanisms and the underlying symptoms.
• AI technologies will not be immune to that tension, and it should be openly acknowledged and addressed during implementation processes.
• In other words, patient-physician trust is vital in improving patient care and the effectiveness of their treatment [105].
• The question of whether AI-based technologies actually bring added value to healthcare with improved outcomes is unanswered.

Four co-authors (TC, TOS, MT, PDN) independently screened the titles and abstracts according to the inclusion and exclusion criteria. A full-text assessment was conducted on these 116 relevant articles, which resulted in 19 included articles, as shown in Figure 2, 11 of which were published in 2020. Moving to a world in which AI can deliver significant, consistent, and global improvements in care will be more challenging. AI is now top-of-mind for healthcare decision makers, governments, investors and innovators, and the European Union itself. An increasing number of governments have set out aspirations for AI in healthcare, in countries as diverse as Finland, Germany, the United Kingdom, Israel, China, and the United States and many are investing heavily in AI-related research. Prediction and assessment of a condition is something that individuals will demand to have more control over in the coming years.

AI policy and the regulatory environment in the united States

Here, variational autoencoders and adversarial autoencoders are often used to design new molecules in an automated process by fitting the design model to large datasets of drug molecules. Autoencoders are a type of neural network for unsupervised learning and are also the tools used to, for instance, generate images of fictional human faces. The autoencoders are trained on many drug molecule structures and the latent variables are then used as the generative model.

These partnerships are not all based on curiosity-driven research but often out of necessity and need of society. In a world where certain expertise is rare, research costs high and effective treatments for certain conditions are yet to be devised, collaboration between various disciplines is key. A good example of this collaboration is seen in a recent breakthrough for antibiotic discovery, where the researchers devised/trained a neural network that actively “learned” the properties of a vast number of molecules in order to identify those that inhibit the growth of E. Another example is the recent research carried out regarding the pandemic of COVID-19 all around the world.

Barriers to AI Implementation in Healthcare

Without a clear understanding, the development of effective implementation strategies will not be possible, nor will AI advance despite the significant investments and possibilities. For a large-scale implementation of AI in healthcare and to qualify for reimbursement on a broad scale across insurance systems, the methods to measure medical and economic outcomes of AI applications have to follow standardized established procedures. The QALY analysis can be conducted based on different questionnaires to fulfill these requirements, and most studies follow the EQ-5D and the SF-6D format (see Appendix in Supplementary Material) (41). The academic literature describes in detail the different technological categories of AI applications, ranging from natural language processing up to expert systems (20). In certain medical sectors, specific types of AI applications are more commonly applied, such as image analysis in radiology or dermatology (21).