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Artificial Intelligence Implementation in Healthcare: A Theory-Based Scoping Review of Barriers and Facilitators PMC

They recognized that it will not be successful if the change is built on individual interests, instead of organizational perspectives. According to the leaders, the county council has focused on building the technical infrastructure that enables the use of AI algorithms. The county council have tried to establish a way of working with multi-professional teams around each application area for AI-based analysis. However, the leaders expressed that it is necessary to look beyond the technology development and plan for the implementation at a much earlier stage in the development process. They believed that their organization generally underestimated the challenges of implementation in practice.

The patient safety movement is already shifting away from blaming individual ‘bad actors’ and working toward identifying systems-wide issues as opportunities for improvement and reduction in potentially avoidable adverse events. The same principles could be applied to AI technology implementation, but where liability will ultimately rest remains to be seen. Perhaps the most powerful role for AI will be as an add-on to or augmentation of human providers.

Current and anticipated investments, top priorities, and risks and concerns with AI

Immune to those variables, AI can predict and diagnose disease at a faster rate than most medical professionals. AI in healthcare shows up in a number of ways, such as finding new links between genetic codes, powering surgery-assisting robots, automating administrative tasks, personalizing treatment options and much more. While Aidoc has a multitude of successful AI implementations, there are other companies that have also stepped up to create remarkable AI solutions that have made a difference ai implementation to healthcare and society. Aidoc, developer of the always-on and intelligent AI platform for radiology departments, has also developed a comprehensive roadmap that outlines precisely what factors have to be addressed in any AI implementation to ensure that it is a success. At Eastern Peak we have strong expertise in building AI solutions for a variety of healthcare domains. Spotting an infected individual in a crowded place can help take instant measures and curb the contamination.

ai implementation in healthcare

Here, DL is used to engineer computer vision algorithms for classifying images of lesions in skin and other tissues. Video data is estimated to contain 25 times the amount of data from high-resolution diagnostic images such as CT and could thus provide a higher data value based on resolution over time. As an example, a video analysis of a laparoscopic procedure in real time has resulted in 92.8% accuracy in identification of all the steps of the procedure and surprisingly, the detection of missing or unexpected steps [26]. Advances in AI have the potential to transform many aspects of healthcare, enabling a future that is more personalised, precise, predictive and portable.

The journey towards sustainable medicines

The robotic apps include those focused on cognitive stimulation, social interaction, as well as general health assessment. Many of these apps use AI-powered tools to process the data collected from the robots in order to perform tasks such as facial recognition, object identification, language processing, and various diagnostic support [59]. As these organizations begin to scale up their AI applications based on their short- and long-term priorities, they should be mindful of risks during implementation. The leaders emphasized the importance of training for implementation of AI systems in healthcare. The county council should provide customized training at the workplace and extra knowledge support for certain professions.

ai implementation in healthcare

Informed patients are more likely to adhere to their treatment regimens and achieve better health outcomes [99]. AI has the potential to play a significant role in patient education by providing personalized and interactive information and guidance to patients and their caregivers [100]. For example, in patients with prostate cancer, introducing a prostate cancer communication assistant (PROSCA) chatbot offered a clear to moderate increase in participants’ knowledge about prostate cancer [101]. Researchers found that ChatGPT, an AI Chatbot founded by OpenAI, can help patients with diabetes understand their diagnosis and treatment options, monitor their symptoms and adherence, provide feedback and encouragement, and answer their questions [102]. AI technology can also be applied to rewrite patient education materials into different reading levels.

Data collection

Sensors that provide the surgeon with finer tactile stimuli are under development and make use of tactile data processing to translate the sensor input into data or stimuli that can be perceived by the surgeon. Such tactile data processing typically makes use of AI, more specifically artificial neural networks to enhance the function of this signal translation and the interpretation of the tactile information [43]. Artificial tactile sensing offers several advantages compared with physical touching including a larger reference library to compare sensation and standardization among surgeons with respect to quantitative features, continuous improvement, and level of training. It uses a deep dynamic memory neural network to read and store experiences and in memory cells. The long short-term memory of the system models the illness trajectory and healthcare processes of users via a time-stamped sequence of events and in this way allows capturing long-term dependencies [41].

  • It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.
  • Boston Dynamics and MIT are ready to provide intelligent robots to perform basic patient care tasks, such as delivering medications, measuring pulses, body temperature scanning, ect.
  • Beth Israel Deaconess Medical Center uses AI to diagnose blood diseases at an early stage using AI-enhanced microscopes.
  • A series of AI-enabled machines can directly question the patient, and a sufficient explanation is provided at the end to ensure appropriate assessment and plan.
  • There will also be a need for quantitative evaluation of the clinical and organizational effects and qualitative assessment that focuses on how healthcare professionals and patients experience the implementation.
  • This capability is particularly vital for addressing common types of drug toxicity, such as cardiotoxicity and hepatotoxicity, which often lead to post-market withdrawal of drugs.

It uses predictive analytics tools and expansive databases, with the ultimate goal of learning more about cancer and developing effective cancer treatments. The company’s AI Recruitment service uses computational algorithms to automate the process of identifying patients who are eligible to be potential candidates for inflammatory bowel disease clinical trials. Iterative Health also produces SKOUT, a tool that uses AI to help doctors identify potentially cancerous polyps. From diagnostics to all-in-one management suits for hospitals and clinics, AI helps deliver better patient care and treatment, automates routine tasks for medical personnel, and delivers personalized healthcare experiences. Another growing focus in healthcare is on effectively designing the ‘choice architecture’ to nudge patient behaviour in a more anticipatory way based on real-world evidence. Through information provided by provider EHR systems, biosensors, watches, smartphones, conversational interfaces and other instrumentation, software can tailor recommendations by comparing patient data to other effective treatment pathways for similar cohorts.

Top challenges include cost of AI solutions, AI integration problems, and AI implementation, data, and risk issues

For example, Partners HealthCare has built an automated response tool to help patients evaluate whether they should be screened for COVID-19. The COVID-19 screener guides users through the simple question and answer procedure, helps them evaluate risks, and suggests a further course of action. With the Earth’s population increasing and many families living in rural areas, a lot of people remain underserved when it comes to the accessibility of medical services. AI-driven telemedicine apps offer a solution to this problem; but apart from picking a clinic in their area, AI also goes a long way in evaluating symptoms and helping choose the right medical professional. There has been considerable attention to the concern that AI will lead to automation of jobs and substantial displacement of the workforce. A Deloitte collaboration with the Oxford Martin Institute26 suggested that 35% of UK jobs could be automated out of existence by AI over the next 10 to 20 years.

Preliminary results demonstrate high accuracy of the AI-generated diagnoses, comparable to that of a trained eye doctor. Not only are data necessary for initial training, a continued data supply is needed for ongoing training, validation, and improvement of AI algorithms. For widespread implementation, data may need to be shared across multiple institutions and potentially across nations.

Using robots to reduce contamination by minimizing human contact

The main problem with AI solutions is that they often don’t have enough data for solving urgent clinical problems, and fail to integrate into the organization’s workflow. The solution would be to build them in close partnership with medical personnel who will be using these solutions. With hospitals fighting a losing battle with the abnormal influx of patients, the need to streamline patient queries became pressing.

ai implementation in healthcare

More recently, IBM’s Watson has received considerable attention in the media for its focus on precision medicine, particularly cancer diagnosis and treatment. Most observers feel that the Watson APIs are technically capable, but taking on cancer treatment was an overly ambitious objective. Watson and other proprietary programs have also suffered from competition with free ‘open source’ programs provided by some vendors, such as Google’s TensorFlow.

More on health care

Qualitative content analysis is widely used in healthcare research [46] to find similarities and differences in the data, in order to understand human experiences [47]. To ensure trustworthiness, the study is reported in accordance with the Consolidated Criteria for Reporting Qualitative Research 32‐item checklist [48]. Much of the initial focus for the application of machine learning in medical imaging has been on image analysis and developing tools to make radiologists more efficient and productive. The same tools will often enable more precise diagnosis and treatment planning or help reduce missed diagnoses, thus leading to improved patient outcomes. Since patients’ health information is protected by law as private and confidential, healthcare providers must comply with strict privacy and data security policies. However, it keeps healthcare practitioners under the ethical & legal obligation not to provide their data to any third party.

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