AI in Healthcare: Revolutionizing Medicine and Saving Lives

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. As an example, the program druGAN used adversarial autoencoders to generate new molecular fingerprints and drug designs incorporating features such as solubility and absorption based on predefined anticancer drug properties. These results suggest a substantial improvement in the efficiency in generating new drug designs with specific properties [21].

However, other studies have suggested that people still prefer human healthcare practitioners over AI, especially for complex or sensitive issues such as mental health, chronic diseases, or end-of-life care [108, 111]. In a US-based study, 60% of participants expressed discomfort with providers relying on AI for their medical care. However, the same study found that 80% of Americans would be willing to use AI-powered tools to help manage their health [109]. Moreover, people’s trust and acceptance of AI may vary depending on their age, gender, education level, cultural background, and previous experience with technology [111, 112]. Artificial intelligence systems powered by machine learning and deep learning are rapidly implemented in medicine.

Clinics & Services

AAL applications typically collect data through sensors and cameras and apply various artificially intelligent tools for developing an intelligent system [52]. A key to delivering this vision will be an expansion of translational research in the field of healthcare applications of artificial intelligence. Alongside this, we need investment into the upskilling of a healthcare workforce and future leaders that are digitally enabled, and to understand and embrace, rather than being intimidated by, the potential of an AI-augmented healthcare system. The rapid progression of AI technology presents an opportunity for its application in clinical practice, potentially revolutionizing healthcare services.

The technique employed by the researchers is often referred to as a sequence modeling, where model sequences of audio and text from patients with and without depression are fed to the system and as these accumulate, various text patterns could be paired with audio signals. For example, words such as “low”, “blue,” and “sad” can be paired with more monotone and flat audio signals. Additionally, the speed and the length of pauses can play a major role in detection of individuals experiencing depression. 2.10

where within a period of 60 seconds and based on the tone and words used, it is possible to measure an estimated emotion. Precision medicine provides the possibility of tailoring healthcare interventions to individuals or groups of patients based on their disease profile, diagnostic or prognostic information, or their treatment response. The tailor-made treatment opportunity will take into consideration the genomic variations as well as contributing factors of medical treatment such as age, gender, geography, race, family history, immune profile, metabolic profile, microbiome, and environment vulnerability.

The rise of artificial intelligence in healthcare applications

AI is not one ubiquitous, universal technology, rather, it represents several subfields (such as machine learning and deep learning) that, individually or in combination, add intelligence to applications. The application of technology and artificial intelligence (AI) in healthcare has the potential to address some of these supply-and-demand challenges. AI algorithms can continuously examine factors such as population demographics, disease prevalence, and geographical distribution. This can identify patients at a higher risk of certain conditions, aiding in prevention or treatment. Edge analytics can also detect irregularities and predict potential healthcare events, ensuring that resources like vaccines are available where most needed.

AI prescription systems are now equipped to deal with non-adherence with medical prescriptions. They do this by studying the patient’s medical history and determining the likelihood that the patient will take the medication as prescribed. Founded in 2004 by Kevin Pho, MD, KevinMD.com is the web’s leading platform where physicians, advanced practitioners, nurses, medical students, and patients share their insight and tell their stories. To train gen-AI models, organizations should also ensure that they are processing data within secure firewalls. Organization leaders may choose to outsource various parts of their tech stack after evaluating their own internal capabilities. At a convention center in Chicago in April, tens of thousands of attendees watched as a new generative-AI (gen AI) technology, enabled by GPT-4, modeled how a healthcare clinician might use new platforms to turn a patient interaction into clinician notes in seconds.

Personalizing Treatment Plans

Large Language Models (LLMs) are a type of AI algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate, and predict new text-based content [1,2,3]. Over the years, AI has undergone significant transformations, from the early days of rule-based systems to the current era of ML and deep learning algorithms [1,2,3]. In healthcare, guidelines usually take much time, from establishing the knowledge gap that needs to be fulfilled to publishing and disseminating these guidelines. AI can help identify newly published data based on data from clinical trials and real-world patient outcomes within the same area of interest which can then facilitate the first stage of mining information. Biopharma company NuMedii has developed Artificial Intelligence for Drug Discovery (AIDD) technology that “employs deep learnings of human biology consisting of hundreds of millions of structured molecular, pharmacological and clinical data points that the company has curated and harmonized. Numerous research investigations focusing on cervical cancer and cervical intraepithelial neoplasia (CIN) have documented the application of AI.

Nevertheless, there are some challenges that need to be considered as AI usage increases in healthcare, such as ethical, social and technical challenges. For example, AI processes may lack transparency, making accountability problematic, or may be biased, leading to unfair, discriminatory behavior or mistaken decisions [94]. Moreover, AI algorithms are unable to perform a holistic approach to clinical scenarios and are not fully able to take into consideration the psychological and social aspects of human nature, which are often considered by a skilled healthcare professional [95]. Addressing those challenges requires collaboration between healthcare professionals, researchers, policymakers and technology developers to ensure that AI tools are implemented responsibly, ethically and safely in the healthcare sector. Since the outbreak of COVID-19 in 2019, AI technologies have experienced accelerated adoption and utilization across various domains within the healthcare sector.

AI in Healthcare Revolutionizing Patient Care

Another example is the recent research carried out regarding the pandemic of COVID-19 all around the world. Predictive Oncology, a precision medicine company has announced that they are launching an AI platform to accelerate the production of new diagnostics and vaccines, by using more than 12,000 computer simulations per machine. This is combined with other efforts to employ DL to find molecules that can interact with the main proteases (Mpro or 3CLpro) of the virus, resulting in the disruption of the replication machinery of the virus inside the host [67], [68]. In the long term, AI systems will become more intelligent, enabling AI healthcare systems achieve a state of precision medicine through AI-augmented healthcare and connected care.

Several professional organizations have developed frameworks for addressing concerns unique to developing, reporting, and validating AI in medicine [69–73]. Instead of focusing on the clinical application of AI, these frameworks are more concerned with educating the technological creators of AI by providing instructions on encouraging transparency in the design and reporting of AI algorithms [69]. The US Food and Drug Administration (FDA) is now developing guidelines on critically assessing real-world applications of AI in medicine while publishing a framework to guide the role of AI and ML in software as medical devices [74]. The European Commission has spearheaded a multidisciplinary effort to improve the credibility of AI [75], and the European Medicines Agency (EMA) has deemed the regulation of AI a strategic priority [76].

Looking Toward the Future

In research, AI has been used to analyze large datasets and identify patterns that would be difficult for humans to detect; this has led to breakthroughs in fields such as genomics and drug discovery. AI has been used in healthcare settings to develop diagnostic tools and personalized treatment plans. As AI continues to evolve, it is crucial to ensure that it is developed responsibly and for the benefit of all [5–8]. The sensors can transmit information to a nearby computing device that can process the data or upload them to the cloud for further processing using various machine learning algorithms, and if necessary, alert relatives or healthcare professionals (Fig. 2.7

). By daily collection of patient data, activities of daily living are defined over time and abnormalities can be detected as a deviation from the routine. Machine learning algorithms used in smart home applications include probabilistic and discriminative methods such as Naive Bayes classifier and Hidden Markov Model, support vector machine, and artificial neural networks [54].

AI can also predict how a new drug will interact with the human body, reducing the time it takes to bring a new medication to market. Oncora aims to provide a single platform where clinicians can find data about cancer patients, clinical outcomes, and treatments so that providers can use collected data to improve future care. The possible applications of AI in health care are extensive, reaching from drug development and imaging to insurance and patient care. Zittrain that image analysis software, while potentially useful in medicine, is also easily fooled.

Improving the precision and reducing waiting timings for radiotherapy planning

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