AI in Healthcare: Revolutionizing Medicine and Saving Lives

It is believed that AI can bring improvements to any process within healthcare operation and delivery. For instance, the cost savings that AI can bring to the healthcare system is an important driver for implementation of AI applications. It is estimated that AI applications can cut annual US healthcare costs by USD 150 billion in 2026.

All these operations are then stacked on top of one another to create layers, sometimes referred to as Deep stacking. This process can be repeated multiple times and each time the image gets filtered more and relatively smaller. The last layer is referred to as a fully connected layer where every value assigned to all layers will contribute to what the results will be. If the system produces an error in this final answer, the gradient descent can be applied by adjusting the values up and down to see how the error changes relative to the right answer of interest. The word “Deep” refers to the multilayered nature of machine learning and among all DL techniques, the most promising in the field of image recognition has been the CNNs.

Generative AI could revolutionize health care — but not if control is ceded to big tech

Some AI programs can also teach themselves to ask new questions and make novel connections between pieces of information. 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. Moreover, Coutre et al. (2018) used image analysis with DL to detect breast cancer histologic subtypes [80].

AI can help remove or minimize time spent on routine, administrative tasks, which can take up to 70 percent of a healthcare practitioner’s time. A recurring theme in interviews was that this type of AI role would not just be uncontroversial but would top of most people’s wish list and would speed up adoption. It can augment a range of clinical activities and help healthcare practitioners access information that can lead to better patient outcomes and higher quality of care. It can improve the speed and accuracy in use of diagnostics, give practitioners faster and easier access to more knowledge, and enable remote monitoring and patient empowerment through self-care. This will all require bringing new activities and skills into the sector, and it will change healthcare education—shifting the focus away from memorizing facts and moving to innovation, entrepreneurship, continuous learning, and multidisciplinary working.

1.2. Artificial intelligence applications in healthcare

In one example, Markov Logic Network was used for activity recognition design to model both simple and composite activities and decide on appropriate alerts to process patient abnormality. The Markov Logic Network used handles both uncertainty modeling and domain knowledge modeling within a single framework, thus modeling the factors that influence patient abnormality [55]. Uncertainty modeling is important for monitoring patients with dementia as activities conducted by the patient are typically incomplete in nature. Domain knowledge related to the patient’s lifestyle is also important and combined with their medical history it can enhance the probability of activity recognition and facilitate decision-making. This machine learning-based activity recognition framework detected abnormality together with contextual factors such as object, space, time, and duration for decision support on suitable action to keep the patient safe in the given environment.

This could allow medical researchers to see a much bigger picture and could provide doctors with much more accurate information, on demand, when treating their patients. 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. Using AI tools, researchers have developed zinc-finger (ZF) editing, a technique that can change and control genes. Although the artificial zinc fingers are challenging to design for a specific task, according to one study published in January 2023, in the future, this technique may help correct diseases caused by multiple genetic factors, from autism to heart disease and obesity. However, if detected and treated at an early stage, many cases of cancers can be healed/cured.

AI algorithms can analyze patient data to assist with triaging patients based on urgency; this helps prioritize high-risk cases, reducing waiting times and improving patient flow [31]. Introducing a reliable symptom assessment tool can rule out other causes of illness to reduce the number of unnecessary visits to the ED. 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. Research on whether people prefer AI over healthcare practitioners has shown mixed results depending on the context, type of AI system, and participants’ characteristics [107, 108]. Some surveys have indicated that people are generally willing to use or interact with AI for health-related purposes such as diagnosis, treatment, monitoring, or decision support [108–110].

Robert Truog, head of the HMS Center for Bioethics, the Frances Glessner Lee Professor of Legal Medicine, and a pediatric anesthesiologist at Boston Children’s Hospital, said the defining characteristic of his last decade in practice has been a rapid increase in information. While more data about patients and their conditions might be viewed as a good thing, it’s only good if it can be usefully managed. The two agree that the biggest impediment to greater use of AI in formulating COVID response has been a lack of reliable, real-time data. Data collection and sharing have been slowed by older infrastructure — some U.S. reports are still faxed to public health centers, Bates said — by lags in data collection, and by privacy concerns that short-circuit data sharing.

Members’ and patients’ personally identifiable information must be protected—a level of security that open-source gen-AI tools may not provide. If the data sets from which a gen-AI-powered platform are based overindex of certain patient populations, then a patient care plan that the platform generates may be biased, leaving patients with inaccurate, unhelpful, or potentially harmful information. And integrating gen-AI platforms with other hospital systems, such as billing systems, may lead to inefficiencies and erroneous expenses if done incorrectly.

• These are highly applicable in identifying key disease detection patterns among big datasets.
• And integrating gen-AI platforms with other hospital systems, such as billing systems, may lead to inefficiencies and erroneous expenses if done incorrectly.
• AI is now top-of-mind for healthcare decision makers, governments, investors and innovators, and the European Union itself.
• Many elderly people experience a decline in their cognitive abilities and have difficulties in problem-solving tasks as well as maintaining attention and accessing their memory.

The objective of precision medicine is to use individual biology rather than population biology at all stages of a patient’s medical journey. This means collecting data from individuals such as genetic information, physiological monitoring data, or EMR data and tailoring their treatment based on advanced models. Advantages of precision medicine include reduced healthcare costs, reduction in adverse drug response, and enhancing effectivity of drug action [11]. Innovation in precision medicine is expected to provide great benefits to patients and change the way health services are delivered and evaluated.

The covid pandemic exposed critical challenges within the health care system — such as health care worker shortages. Finding new interventions is one thing; designing them so health professionals can use them is another. Doshi-Velez’s work centers on “interpretable AI” and optimizing how doctors and patients can put it to work to improve health.

AI in enhancing patient education and mitigating healthcare provider burnout

These influencers and health IT leaders are change-makers, paving the way toward health equity and transforming healthcare’s approach to data. Many chatbots are a form of “generative AI.” This type of AI can create new content based on what it learns from analyzing existing data. Such chatbots use what’s called large language models, which are trained on huge data sets that are gathered from across the internet. It concludes that automation will affect most jobs across sectors, but the degree varies significantly, and healthcare is one of the sectors with the lowest overall potential for automation—only 35 percent of time spent is potentially automatable and this varies by type of occupation.

Software trained on data sets that reflect cultural biases will incorporate those blind spots. AI designed to both heal and make a buck might increase — rather than cut — costs, and programs that learn as they go can produce a raft of unintended consequences once they start interacting with unpredictable humans. An everyday example of artificial intelligence in health care is personal health monitoring. Drug development is a tedious venture that may take years and thousands of failed attempts.

• Moreover, Coutre et al. (2018) used image analysis with DL to detect breast cancer histologic subtypes [80].
• Innovations in natural language processing and AI-driven robotics will likely play a larger role in patient care, and AI will continue to enhance decision support systems for clinicians.
• It is designed to simulate human conversation to offer personalized patient care based on input from the patient [83].
• In the future, AI technology could be used to support medical decisions by providing clinicians with real-time assistance and insights.
• There is therefore a need for a fresh approach and AI promises to be the tool to be used to fill this demand gap.

The United States still dominates the list of firms with highest VC funding in healthcare AI to date, and has the most completed AI-related healthcare research studies and trials. But the fastest growth is emerging in Asia, especially China, where leading domestic conglomerates and tech players have consumer-focused healthcare AI offerings and Ping An’s Good Doctor, the leading online health-management platform already lists more than 300 million users. Yet, at the same time, valuable data sets are not linked, with critical data-governance, access, and security issues still needing to be clarified, delaying further adoption. European investment and research in AI are strong when grouped together but fragmented at the country or regional level.

The improvements will not only be in the health care industry but in other areas as well. Late last year, Google’s DeepMind trained a neural network to accurately detect over 50 types of eye diseases by simply analyzing 3D rental scans. Ensuring transparency, accountability, and public trust in AI-driven health care solutions is crucial for their widespread adoption. Even with all the precautions that applying gen AI to the healthcare industry necessitates, the possibilities are potentially too big for healthcare organizations to sit it out. While experimenting with AI, healthcare organizations should be able to adopt approaches to protect consumers and patients in ways that still align to the views of regulators.

Nevertheless, the ability to provide real-time recommendations relies on the advancement of ML algorithms capable of predicting patients who may require specific medications based on genomic information. The key to tailoring medications and dosages to patients lies in the pre-emptive genotyping of patients prior to the actual need for such information [49, 50]. Emergency department providers understand that integrating AI into their work processes is necessary for solving these problems by enhancing efficiency, and accuracy, and improving patient outcomes [28, 29]. Additionally, there may be a chance for algorithm support and automated decision-making to optimize ED flow measurements and resource allocation [30].

AI systems today are beginning to be adopted by healthcare organisations to automate time consuming, high volume repetitive tasks. Moreover, there is considerable progress in demonstrating the use of AI in precision diagnostics (eg diabetic retinopathy and radiotherapy planning). The projected benefits of using AI in clinical laboratories include but are not limited to, increased efficacy and precision. Automated techniques in blood cultures, susceptibility testing, and molecular platforms have become standard in numerous laboratories globally, contributing significantly to laboratory efficiency [21, 25].

For these patients, this immersive experience could act as a personal rehabilitation physiotherapist who engages their upper limb movement multiple times a day, allowing for possible neuroplasticity and a gradual return of normal motor function to these regions. Furthermore, CNNs require a significant amount of training data that comes in the form of medical images along with labels for what the image is supposed to be. At each hidden layer of training, CNNs can adjust the applied weights and filters (characteristics of regions in an image) to improve the performance on the given training data.

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