Revolutionizing healthcare: the role of artificial intelligence in clinical practice

Mood and mental health-related conditions are immensely important topic in today’s world and for good reason. According to the WHO, one in four people around the world experiences such conditions and as a result can accelerate their path toward ill-health and comorbidities. Recently, machine learning algorithms have been developed to detect words and intonations of an individual’s speech that may indicate a mood disorder. Using neural networks, an MIT-based lab has conducted research onto the detection of early signs of depression using speech. According to the researchers, the “model sees sequences of words/speaking style” and decides whether these emerging patterns are likely to be seen in individuals with and without depression [63].

In medical research, scientists collect and analyze huge amounts of data using statistical methods. Given that this process is often time-consuming and costly, adopting AI algorithms can accelerate the research by optimizing study design, patient recruitment and revealing deeper insights about diseases and treatments. The COVID-19 pandemic accelerated the adoption of telemedicine, and AI played a pivotal role in making this transition seamless. Telehealth applications often include AI-driven symptom checkers, providing patients with immediate information and guidance. Remote patient monitoring, coupled with AI, allows physicians and Nurse Practitioners to keep a closer eye on patients with chronic conditions, intervening promptly when necessary. Machine learning algorithms can sift through vast chemical libraries to identify potential drug candidates.

AI in Healthcare: Revolutionizing Medicine and Saving Lives

It is predominantly utilized for drugs with a narrow therapeutic index to avoid both underdosing insufficiently medicating as well as toxic levels. TDM aims to ensure that patients receive the right drug, at the right dose, at the right time, to achieve the desired therapeutic outcome while minimizing adverse effects [56]. The use of AI in TDM has the potential to revolutionize how drugs are monitored and prescribed.

Overall, there is a significant opportunity for EU health systems, but AI’s full potential remains to be explored and the impact on the ground remains limited. A surprising 44 percent of the healthcare professionals we surveyed—and these were professionals chosen based on their engagement with healthcare innovation—had never been involved in the development or deployment of an AI solution in their organization. In the modern healthcare system, AI and Digital Pathology (DP) have the potential to challenge traditional practice and provide precision for pathology diagnostics.

AI and Clinical Practice—the Learning Health System and AI

Compared with more conventional machine learning approaches, DL models take a long time to train because of the large datasets and the often large number of parameters needed. There is therefore ongoing work on reducing the amount of data required as training sets for DL so it can learn with only small amounts of available data. This is similar to the learning process that takes place in the human brain and would be beneficial in applications where data collection is resource intensive and large datasets are not readily available, as is often the case with medicinal chemistry and novel drug targets.

Artificial intelligence stands to revolutionize health and medicine—from advancing drug discovery to personalizing patient care. As AI innovation keeps accelerating, we must act now to ensure it advances with intention and purpose. The AI model identified with accuracy large cancerous lung nodules, a result that enables doctors to make quicker decisions about medium-risk patients who have abnormal growths on their CT scans. This enables early-stage diagnosis that increases the five-year survival rate compared to those whose cancer is detected at a later stage. Whether it is insurers getting billed for services not rendered, faulty test kits or devices or surgeons conducting unnecessary operations to obtain higher insurance payments, AI helps detect fraud by processing extensive medical and billing data in search of deviations and irregular patterns. AI can spot and duplicate billing, helping prevent fraud and ensuring patients benefit from appropriate care.

Companies like Ezra use full-body MRI scans to assist medical professionals in early detection of cancer, while Zebra Medical Vision uses AI-driven tools to detect potential osteoporosis in X-rays and potential breast cancer in mammograms. Healthtech company Komodo compiles clinical encounters of 330m patients who have gone through the health care system. This map of data allows Komodo to offer its customers — which span from disease advocacy groups to pharmaceutical companies — clinical insights gathered through analytics. CloudMedX is a computing platform that streamlines clinical processes and improves patient outcomes using predictive analytics.

Et al. (2022), within the last two decades, AI began to incorporate neuroimaging studies of psychiatric patients with deep learning models to classify patients with psychiatric disorders [53]. Et al. (2017) were able to classify schizophrenia patients and controls with an accuracy of 85.5% by extracting functional connectivity patterns from resting-state functional MRIs of schizophrenia patients and healthy controls [54]. Researchers at the Vanderbilt University Medical Centre created machine-learning algorithms that achieved 80–90% accuracy when predicting whether someone will attempt suicide within the next 2 years, and 92% accuracy in predicting whether someone will attempt suicide within the next week [1].

Institutional Review Board Statement

With the aging society, more and more people live through old age with chronic disorders and mostly manage to live independently up to an old age. Data indicates that half of people above the age of 65 years have a disability of some sort, which constitutes over 35 million people in the United States alone. Most people want to preserve their autonomy, even at an old age, and maintain control over their lives and decisions [50]. Assistive technologies increase the self-dependencies of patients, encouraging user participation in Information and Communication Technology (ICT) tools to provide remote care services type assistance and provide information to the healthcare professionals. Assistive technologies are experiencing rapid growth, especially among people aged 65–74 years [51]. Governments, industries, and various organizations are promoting the concept of AAL, which enables people to live independently in their home environment.

Fraud is affecting healthcare systems at different levels and stakeholders have already begun to use AI algorithms as a tool to fight against it. Scopio Labs for example is the developer of full-field cell morphology – an AI-driven imaging platform that scans and shares in real time blood samples at high resolution. DeepMind, acquired by Google Health in 2014, aims to create intelligent AI systems across industries, but has made notable strides in health care specifically. When the company saw providers were still manually calculating the size of heart ventricles, it combined deep learning with cloud computing GPUs to automatically measure ventricles.

Designers specializing in human-machine interactions on clinical decision making will help create new workflows that integrate AI. Data architects will be critical in defining how to record, store and structure clinical data so that algorithms can deliver insights, while leaders in data governance and data ethics will also play vital roles. In other data-rich areas, such as genomics, new professionals would include ‘hybrid’ roles, such as clinical bioinformaticians, specialists in genomic medicine, and genomic counsellors.

• With the recent public launch of large language model chatbots like ChatGPT, the buzz around how the health care industry can best make use of artificial intelligence is reaching a crescendo.
• However, it is pivotal to note that the success of predictive analytics in public health management depends on the quality of data and the technological infrastructure used to develop and implement predictive models.
• The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.
• Remote patient monitoring, coupled with AI, allows physicians and Nurse Practitioners to keep a closer eye on patients with chronic conditions, intervening promptly when necessary.

According to Oncora, oncologists log in to at least six different software systems when treating their patients. In addition, AI technologies can help with early diagnosis, imaging, emergency call triage, and much more. For example, a new system created by Johns Hopkins University detects sepsis hours earlier than traditional methods and reduces the chance of patient death by 20%. Sepsis, usually caused by infection, is easy to miss in a crowded emergency room as it presents with common, nonspecific symptoms such as fever and confusion. “We ensured the data set is of high quality, enabling the AI system to achieve a performance similar to that of radiologists,” Lee said. Jha said a similar scenario could play out in the developing world should, for example, a community health worker see something that makes him or her disagree with a recommendation made by a big-name company’s AI-driven app.

In addition, AI algorithms have been developed to provide quantitative measurements of immunohistochemically stained Ki-67 [81], ER [80], PR, and Her-2/neu images [82]. These use various operating systems and approaches with more than a thousand EMR providers operating in the United States alone. Integration of EMR records on their own poses a great challenge and interoperability of these systems is important to obtain the best value from the data. There are various international efforts in gathering EMR data across countries including Observational Health Data Science and Informatics (OHDSI), who have consolidated 1.26 billion patient records from 17 different countries [40]. Various AI methods have been used to extract, classify, and correlate data from EMRs but most generally make use of NLP, DL, and neural networks. One application of NLP is disease classification based on medical notes and standardized codes using International Statistical Classification of Diseases and Related Health Problems (ICD).

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