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Advancing Patient Care: How Artificial Intelligence Is Transforming Healthcare PMC

Many pharmaceutical giants like Sanofi and Pfizer are teaming up with tech companies IBM and Google. To weigh the value of gen-AI applications in healthcare against the risks, leaders should create risk and legal frameworks that govern the use of gen AI in their organizations. Data security, bias and fairness, and regulatory compliance and accountability should all be considered as part of these frameworks. Clinical operations are another area ripe for the potential efficiencies that gen AI may bring. Today, hospital providers and administrative staff are required to complete dozens of forms per patient, not to mention post-visit notes, employee shift notes, and other administrative tasks that take up hours of time and can contribute to hospital employee burnout. Within hospitals and physician groups, gen-AI technology has the potential to affect everything from continuity of care to clinical operations and contracting to corporate functions.

• She received an MD from Harvard Medical School, an MBA from Harvard Business School and an MPH from the Harvard T.H. Chan School of Public Health.
• Here, by wearing a smart device or manual data entry for a prolonged period, individuals will be able to communicate to their healthcare workers without the need of disrupting their daily lives [35].
• A smart home is a normal residential home, which has been augmented using different sensors and monitoring tools to make it “smart” and facilitate the lives of the residents in their living space.
• Although the applications of AI for EMRs are still quite limited, the potential for using the large databases to detect new trends and predict health outcomes is enormous.

This approach achieves high-accuracy predictions by primarily relying on personal health information even before the onset of the disease, eliminating the necessity for invasive or costly procedures such as endometrial biopsy [88]. We conducted a comprehensive review of current literature including original articles that studied various clinical applications of AI in healthcare. We performed extensive searches on Google Scholar, PubMed, and ScienceDirect databases to identify relevant manuscripts. As keywords, we used “artificial intelligence”, “deep learning”, and “machine learning”, combined with “clinical applications”, and “healthcare”.

Potential uses of generative AI in healthcare

Oncora’s analytics software pulls in data from separate systems, allowing providers to access everything in one place. It listens alongside health care workers during patient visits through proprietary speech recognition and language processing tech. For AI to achieve its promise in health care, algorithms and their designers have to understand the potential pitfalls.

Precision medication refers to dispensing the correct treatment depending on the patient’s characteristics and behavior. Medical technology has come a long way from the humble beginnings of medical instruments and early imaging techniques like X-rays. The 20th and 21st centuries have seen the introduction of groundbreaking innovations, such as magnetic resonance imaging (MRI), computed tomography (CT) scans, and minimally invasive surgical procedures. Organizations that can implement gen AI quickly are likely to be in the best position to see benefits, whether in the form of better efficiency or improved outcomes and experience.

Data Availability Statement

NLP is crucial for many applications of big data analysis within healthcare, particularly for EMRs and translation of narratives provided by clinicians. It is typically used in operations such as extraction of information, conversion of unstructured data into structured data, and categorization of data and documents. Many data and computation-based technologies have followed exponential growth trajectories. The most known example is that of Moore’s law, which explains the exponential growth in the performance of computer chips. Many consumer-oriented apps have experienced similar exponential growth by offering affordable services.

Table 1 provides an overview of the definitions of terms related to AI and their integration within the healthcare sector. Smart homes can be useful for people with dementia and several studies have investigated smart home applications to facilitate the lives of dementia patients. Low-cost sensors in an Internet of Things (IoT) architecture can be a useful way of detecting abnormal behavior in the home. For instance, sensors are placed in different areas of the house including the bedroom, kitchen, and bathroom to ensure safety. A sensor can be placed on the oven and detect the use of the cooker, so the patient is reminded if it was not switched off after use.

Tackling healthcare’s biggest burdens with generative AI

One recent area where AI’s promise has remained largely unrealized is the global response to COVID-19, according to Kohane and Bates. Bates, who delivered a talk in August at the Riyad Global Digital Health Summit titled “Use of AI in Weathering the COVID Storm,” said though there were successes, much of the response has relied on traditional epidemiological and medical tools. Thanks to the internet of medical things (IoMT) and advanced AI, there is a host of consumer-oriented products geared to promoting good health. Over the last few years, we have seen mobile apps, wearables, and discrete monitors that continually collect data and check the vitals.

A rain sensor can be placed by the window to alert the patient if the window was left open during rain. A bath sensor and a lamp sensor can be used in the bathroom to ensure that they are not left on [53]. At first look, a wearable device might look like an ordinary band or watch; however, these devices bridge the gap between multiple scientific disciplines such as biomedical engineering, materials science, electronics, computer programming, and data science, among many others [33].

Applying gen AI to healthcare businesses could help transform the industry, but only after leaders take inventory of their own operations, talent, and technological capabilities. Gen AI represents a meaningful new tool that can help unlock a piece of the unrealized $1 trillion of improvement potential present in the industry. If healthcare leaders can demystify the tech and secure the checks and balances within healthcare through third-party accountability, we can align the goals of industry, consumers and regulators.

While there are 100+ of AI health care startups, these 11 companies are paving the way with groundbreaking research and technology. With the cost of developing a prescription drug rising 145% since 2003 to a staggering $2.6B, using machine learning to repurpose existing drug trials could save time, money, and, most importantly, lives. Dr. Marinka Zitnik, an assistant professor of biomedical informatics at Harvard Medical School, says two uses — precision medicine and therapeutic science — stand out above the crowd. From new body imaging devices to oncology platforms, some trailblazing AI development services companies are already changing how millions of people receive care.

Read more about AI for Way to Revolutionize Medicine here.

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