AI Innovations & the Future of Health Care

WHO recognizes that artificial intelligence (AI) holds great promise for pharmaceutical

development and delivery. Artificial Intelligence (AI) refers to the capability of algorithms integrated into systems

and tools to learn from data so that they can perform automated... This section discusses articles on AI in healthcare in terms of single or multiple publications in each country.

With AI-powered remote monitoring systems, patients can have their vital signs tracked and monitored, alerting healthcare providers to any potential issues. This can lead to earlier intervention and improved patient outcomes, as well as reducing the need for in-person visits to healthcare facilities. Virtual consultations are another way in which AI is being used to improve the delivery of healthcare. By providing remote medical care, patients can receive medical treatment without having to travel to a healthcare facility. This can be especially beneficial for those who live in remote areas or who have mobility issues.

AI is a powerful tool, and people are learning how to make the best use of it every day. This chatbot was built using EleutherAI’s GPT-J, a model akin to the widely-known ChatGPT from OpenAI. Thus, while integrating AI can offer great benefits, understanding its limitations and risks is crucial. In one distressing instance, a man from Belgium took his own life following prolonged interaction with an AI chatbot, discussing the climate crisis. The digital bill of rights pushes algorithm designers and software coders to have the backs of communities against algorithmic discrimination. It calls for fairness in ensuring access for people with disabilities, running disparity tests, and putting the test results out there for everyone to see.

Examples of the Types of Positive Patient Feedback Your Organization Needs

These endeavors are necessary for generating the comprehensive data required to train the algorithms effectively, ensure their reliability in real-world settings, and further develop AI-based clinical decision tools. Artificial intelligence (AI) generally applies to computational technologies that emulate mechanisms assisted by human intelligence, such as thought, deep learning, adaptation, engagement, and sensory understanding [1, 2]. Some devices can execute a role that typically involves human interpretation and decision-making [3, 4]. These techniques have an interdisciplinary approach and can be applied to different fields, such as medicine and health. AI has been involved in medicine since as early as the 1950s, when physicians made the first attempts to improve their diagnoses using computer-aided programs [5, 6].

A bulk of sensitive patient data is generated and processed with the use of AI tools. Thus, you need a high level of protection from any breaches and other vulnerabilities in order to avoid potential losses that leaks can incur. Let’s first take a closer look at the advantages of artificial intelligence in healthcare to determine why you should be interested in pursuing this type of development. It is almost an impossible quest for humans in the medical sector to keep abreast with the increasing inflow of information about health conditions, treatments, and medical technology. AI operates as a helpful and effective second opinion when it comes to detecting the problematic regions or lesions that otherwise might be overlooked.

Now, with generative AI, health care providers might also lean heavily on AI-assisted decision-making. Most experts agree that AI will not replace doctors or other healthcare professionals, and it’s unlikely that patients will be scheduling visits with a ChatGPT-like bot anytime soon. Instead, AI technology will be used to enhance processes and workflows, improve quality, and assist with making sense of the massive sets of patient data that exist in healthcare organizations. Moreover, AI provides patients in developing countries with access to professional treatment.

One of the key ways that AI can help is by detecting and preventing errors in medical care. AI algorithms can be trained to analyse medical records, identifying errors or potential risks such as misdiagnoses, incorrect treatments, or adverse events. This information can be used to help doctors prevent similar errors from happening in the future. AI algorithms can be designed to provide doctors with real-time guidance and recommendations based on patient data, helping them to make informed decisions and reducing the risk of errors.

Treatments are often highly individualized, which does not align with AI’s strengths in high-repetition, low-risk tasks. Given these complexities, the integration of AI into medical treatment processes appears unlikely in the near future. In a study of a social media forum, most people asking healthcare questions preferred responses from an AI-powered chatbot over those from physicians, ranking the chatbot’s answers higher in quality and empathy. However, the researchers conducting this study emphasize that their results only suggest the value of such chatbots in answering patients’ questions, and recommend it be followed up with a more convincing study. AI also can help promote information on disease prevention online, reaching large numbers of people quickly, and even analyze text on social media to predict outbreaks.

In recent years, AI has been used to improve the delivery of healthcare in a variety of ways, from providing personalized health information to enabling virtual consultations and remote monitoring. The joint ITU-WHO Focus Group on Artificial Intelligence for Health (FG-AI4H) has built a platform - known as the ITU-WHO AI for Health Framework - for the testing and benchmarking of AI applications in health domain. As of November 2018, eight use cases are being benchmarked, including assessing breast cancer risk from histopathological imagery, guiding anti-venom selection from snake images, and diagnosing skin lesions. In pursuing the philosophy of Massaro et al.’s [11] methodological article, we have climbed on the shoulders of giants, hoping to provide a bird's-eye view of the AI literature in healthcare.

Jesse Corn, CPO Zivian Health, is a digital health executive and health tech founder with over 14 years of experience in digital solutions. Leads the effort to explore potential opportunities, develop a cogent AI strategy and harness the necessary funding, professionals, technology and organizational resources to implement them. Availability of financial support and adequate infrastructural facilities is important to ensure their participation in AI projects.

Top applications of AI in medical imaging include cardiovascular imaging, lung imaging, neurological imaging, and breast imaging. These applications not only help in the early diagnosis of diseases but also assist in continuous monitoring and adaptive treatment. These include the diagnosis of diseases, medical imaging, patient care, medication allocation, healthcare research, surgery, pandemic spread prediction, and many more. The Internet of Things (IoT), powered by AI and machine learning capabilities, makes it easier than ever for patients to be proactive participants in their own health care. From accessible EHR information through online platforms to sharing personal health data from wearable devices, technology-driven opportunities for patient engagement continue to expand.

What Are The Benefits Of AI in Healthcare?

You might have watched a crazy video of a surgeon using an AI tool during an operation, right? As more critical activities are automated, physicians have more time to examine patients and identify sickness and disease. According to a recent survey by Business Wire, the investments in artificial intelligence for healthcare will surpass 34 billion dollars by 2025! Here are just some of the many ways AI is impacting the health care field for the better. To look at the big picture of medical AI, it’s important to see pros and cons of AI in healthcare.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Telehealth solutions are being implemented to track patient progress, recover vital diagnosis data and contribute population information to shared networks. With AI, health providers can identify and address mistaken claims before insurance companies deny payment for them. Not only does this streamline the claims process, AI saves hospital staff the time to work through the denial and resubmit the claim.

Just think about it, a population of citizens that has at their finger tips a long arm that responds to their basic health check up at any one time, any one day. This is the reality with AI-driven chatbots and virtual assistants, and it is high time that marketers adapted their thinking and strategies to this new reality. Some of them are trained on large repositories of medical information, can answer simple questions about patients’ health, assign appointments, or remind about the administration of prescribed medication. As well as providing thought leadership around AI in healthcare, we are developing new products and services that deliver cutting-edge technology to transform healthcare. Our joint publication with McKinsey & Company explores the impact of AI on healthcare practitioners, and the implications of introducing and scaling AI for healthcare organisations and healthcare systems across Europe.

The following sub-sections start with an analysis of the total number of published articles. Now that we know the role of AI in healthcare in the field of Medical Data Analysis. Healthcare entities and their third-party vendors are particularly vulnerable to data breaches and ransomware attacks. The healthcare industry, which is especially vulnerable to attack, also reported the most expensive data breaches, with an average cost of $10.93 million, according to IBM Security’s Cost of a Data Breach Report for 2023.

Integrating AI with wearable devices, electronic health records, and telemedicine platforms has the potential to enhance personalized healthcare delivery. (1) AI will aid in nation-wide research and cooperation that will provide an impetus for the development of imaging science and decentralization of medical services. (2) AI may help to bridge the gap for need of specialized medical personnel in the peripheral areas in developing countries like India. (3) Government initiatives, ethical considerations and joint public private sector collaborations will ensure smooth transition and implementation of AI in healthcare especially in radiology. People with specific family medical histories and records can get highly detailed diagnoses and treatments.

One IBM client has developed a predictive AI model for premature babies that is 75% accurate in detecting severe sepsis. In addition, AI algorithms can help health care providers by providing real-time data and recommendations. For example, algorithms can monitor patients’ vital signs, such as heart rate and blood pressure, and alert doctors if there is a sudden change.

However, if AI systems are not trained with enough data from diverse backgrounds, there is a significant risk of defective diagnosis. Unless AI is explainable, doctors are not experienced enough in AI to recognize a mistake. If there is an incorrect diagnosis, questions are then raised around accountability.

However, more data are emerging for the application of AI in diagnosing different diseases, such as cancer. A study was published in the UK where authors input a large dataset of mammograms into an AI system for breast cancer diagnosis. This study showed that utilizing an AI system to interpret mammograms had an absolute reduction in false positives and false negatives by 5.7% and 9.4%, respectively [11]. Another study was conducted in South Korea, where authors compared AI diagnoses of breast cancer versus radiologists.

For this purpose, we benefit from the analysis of Zupic and Čater [15], who provide several research questions for future researchers to link the study of authors, journals, keywords and citations. Therefore, RQ1 is “What are the most prominent authors, journal keywords and citations in the field of the research study? ” Additionally, as suggested by Haleem et al. [35], new technologies, including AI, are changing the medical field in unexpected timeframes, requiring studies in multiple areas. Therefore, RQ2 is “How does artificial intelligence relate to healthcare, and what is the focus of the literature? ” Then, as discussed by Massaro et al. [36], RQ3 is “What are the research applications of artificial intelligence for healthcare?

A second, but equally important subset of AI known as natural language processing, or NLP, makes it easier than ever to automate many of the complex, time-consuming, repetitive tasks that eat up a lot of resources in health care administration. With NLP, health care organizations can dramatically increase efficiency and accuracy in critical areas of care. AI creates an opportunity to customize patient management, especially using telemedicine solutions.

This discussion guide identifies issues and key strategic questions leaders should consider to successfully integrate AI-powered technologies into their care delivery operations. AI has potential to change the medical industry in the future for good, but it’ll likely always require human interaction. From patient empathy to critical reasoning, there are certain skills that can’t be achieved with 1s and 0s. When considering adopting AI technology, it’s important to weigh the risks against the benefits of AI in healthcare. While developers work to offset these risks, we must acknowledge that AI programs can’t think critically about how they function.

Integrating AI in virtual health and mental health support has shown promise in improving patient care. However, it is important to address limitations such as bias and lack of personalization to ensure equitable and effective use of AI. Several professional organizations have developed frameworks for addressing concerns unique to developing, reporting, and validating AI in medicine [69,70,71,72,73]. 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].

Ways to Mitigate Breach Risk

AI algorithms can analyze vast datasets of molecular information, predict the effectiveness of compounds, and identify potential side effects. In addition to infectious diseases, AI is instrumental in forecasting the progression of chronic illnesses in individuals. By identifying risk factors and providing early warnings, AI empowers healthcare providers to implement preventive measures, ultimately reducing the burden on healthcare systems. AKASA’s AI platform helps healthcare providers streamline workflows by automating administrative tasks to allow staff to focus where they’re needed. The automation can be customized to meet a facility’s particular needs and priorities, while maintaining accuracy for managing claims, payments and other elements of the revenue cycle. Greenlight Guru, a medical technology company, uses AI in its search engine to detect and assess security risks in network devices.

The collected data must be preprocessed before it can be used to train an algorithm. The raw data that has been collected often contains errors due to manual entry of data or a variety of other reasons. These entries are sometimes modified through mathematical justification or are simply removed. Care should be taken that data preprocessing does not result in a biased pool of data. Contact tracing is a disease control measure used by government authorities to limit spread of a disease. Contact tracing works by contacting and informing individuals that have been exposed to a person who has contracted the disease and instructing them to quarantine to prevent further spread of the disease.

However, as Meskò et al. [7] find, the technology will potentially reduce care costs and repetitive operations by focusing the medical profession on critical thinking and clinical creativity. As Cho et al. and Doyle et al. [8, 9] add, the AI perspective is exciting; however, new studies will be needed to establish the efficacy and applications of AI in the medical field [10]. AI-powered ultrasound technology offers the potential to speed https://chat.openai.com/ up the widespread application of medical ultrasound in a range of clinical contexts. AI models can account only for information ‘seen’ during training, so in this example, non‐imaging clinical information is not taken into account by the AI model. Hence, an important emerging area of healthcare AI research focuses on building AI models that integrate imaging and electronic health record data for ‘personalized diagnostic imaging’.

Patient engagement is a critical aspect of healthcare, influencing treatment adherence and overall outcomes. AI-driven healthcare apps and platforms are designed to engage patients actively in their healthcare journey. After adopting the AI Agents, Behavioral Healthworks was able to reduce its full-time employees for billing and payment processing tasks. They went from four or five teammates to just one who uses Thoughtful AI's platform.

How can AI technology advance medicine and public health?

Based on the user’s vitals, the device can detect the tell-tale signs of a serious health event. Furthermore, AI can analyze billions of compounds for drug testing, condensing research that would typically take years into only a few weeks. Researchers can review the virus genomes alongside AI to develop vaccines quickly and prevent disease. For instance, in the case of the COVID-19 pandemic, AI has assisted biomedical scientists in the research and development of vaccination.

Studies have also found that AI tools can re-identify individuals whose data is held in health data repositories even when the data has been anonymized and scrubbed of all identifiers. In some instances, the AI can Chat GPT not only re-identify the individual, it can make sophisticated guesses about the individual’s non-health data. Several measures must be taken to ensure responsible and effective implementation of AI in healthcare.

In conclusion, the integration of Artificial Intelligence (AI) in medical and dental education has the potential to revolutionize the way in which healthcare professionals are trained. From AI-powered virtual patients for hands-on training, to AI-generated exam questions for objective assessment, the applications of AI in healthcare education are numerous and exciting. However, as with any new technology, there is a need for ongoing research and regulation to ensure that the benefits of AI are maximized, and the potential risks are minimized. One of the biggest challenges facing the use of AI in healthcare education is the need for high-quality data to train AI algorithms. Public perception of the benefits and risks of AI in healthcare systems is a crucial factor in determining its adoption and integration.

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This review article aims to explore the current state of AI in healthcare, its potential benefits, limitations, and challenges, and to provide insights into its future development. By doing so, this review aims to contribute to a better understanding of AI’s role in healthcare and facilitate its integration into clinical practice. In the realm of healthcare, time is often a critical factor in determining patient outcomes.

Through wearable sensors and internet-connected devices, AI algorithms can assist in continuous remote patient monitoring. Like every other industry, artificial intelligence (AI) is rapidly transforming the landscape of healthcare and medicine. This emerging technology and its capabilities can revolutionize medicine by redefining the doctor-patient relationship and could save the healthcare industry $360 billion a year, according to McKinsey and Harvard. As AI becomes more important in healthcare delivery and more AI medical applications are developed, ethical, and regulatory governance must be established.

The company develops AI tools that give physicians insights into treatments and cures, aiding in areas like radiology, cardiology, and neurology. With the goal of improving patient care, Iodine Software is creating AI-powered and machine-learning solutions for mid-revenue cycle leakages, like resource optimization and increased response rates. The company’s CognitiveML product discovers client insights, ensuriodes documentation accuracy and highlights missing information. Its RadOncAI tool uses AI to create a radiation therapy plan, homing in on tumors while limiting cancer patients’ exposure as much as possible.

Precision medicine and clinical decision support

One Drop provides a discreet solution for managing chronic conditions like diabetes and high blood pressure, as well as weight management. Qventus is an AI-based software platform that solves operational challenges, including those related to emergency rooms and patient safety. The company’s automated platform can prioritize patient illness and injury and tracks hospital waiting times to help hospitals and health systems optimize care delivery. Spring Health offers a mental health benefit solution employers can adapt to provide their employees with the resources to keep their mental health in check. The technology works by collecting a comprehensive dataset from each individual and comparing that against hundreds of thousands of other data points.

For example, one healthcare system noted a savings of $3 to $4 per visit when they changed to an automated scheduling system. Before jumping into the role of AI in healthcare, it’s important to understand what defines artificial intelligence. The original concept of AI dates back to 1956, when John McCarthy described it as the science and engineering of making intelligent machines. On a big picture level, AI refers to technology that is able to perform tasks that typically require a human level of intelligence and insight.

We are likely to encounter many ethical, medical, occupational and technological changes with AI in healthcare. It is important that healthcare institutions, as well as governmental and regulatory bodies, establish structures to monitor key issues, react in a responsible manner and establish governance mechanisms to limit negative implications. This is one of the more powerful and consequential technologies to impact human societies, so it will require continuous attention and thoughtful policy for many years. Providers and hospitals often use their clinical expertise to develop a plan of care that they know will improve a chronic or acute patient's health. However, that often doesn't matter if the patient fails to make the behavioural adjustment necessary, eg losing weight, scheduling a follow-up visit, filling prescriptions or complying with a treatment plan.

It can help in providing a primary level of care so that doctors and nurses alike can shower their attention on complicated patients thus leading to a better quality of care. More importantly, HIEs could offer AI as a shared service to their affiliates, ensuring that all member entities, regardless of size, can benefit from insights drawn from larger datasets. Such a collaborative approach could help level the playing field, allowing smaller providers to enhance their service quality through AI. This would contribute to a more equitable health care landscape where technology serves as a bridge rather than a barrier. While the application of generative AI in health care has yielded promising results, it is crucial to recognize that this technology is not a panacea.

These algorithms can predict the human side effects of certain chemical compounds, speeding up the approval process. It’s saved doctors an average of seven minutes per visit, freeing them from documenting care during or after patient visits. He uses importance of ai in healthcare asthma treatment as an example, saying it can only be effective if personalized – something AI can help with. Diagnoss’ AI medical coding engine checks doctors’ notes in real-time and suggests the right codes, reducing coding errors on claims.

Pfizer uses AI to aid its research into new drug candidates for treating various diseases. For example, the company used AI and machine learning to support the development of a Covid-19 treatment called PAXLOVID. Scientists at Pfizer are able to rely on modeling and simulation to identify compounds that have the highest likelihood of being effective treatment candidates so they can narrow their efforts. Clinical trial efficiency

A lot of time is spent during clinical trials assigning medical codes to patient outcomes and updating the relevant datasets. AI can help speed this process up by providing a quicker and more intelligent search for medical codes.

This form of AI in healthcare is quickly becoming a must-have in the modern healthcare industry and is likely to become even more sophisticated and be used in a wider range of applications. A recent study found that 83% of patients report poor communication as the worst part of their experience, demonstrating a strong need for clearer communication between patients and providers. AI technologies like natural language processing (NLP), predictive analytics, and speech recognition might help healthcare providers have more effective communication with patients. AI might, for instance, deliver more specific information about a patient’s treatment options, allowing the healthcare provider to have more meaningful conversations with the patient for shared decision-making. We believe that AI has an important role to play in the healthcare offerings of the future. In the form of machine learning, it is the primary capability behind the development of precision medicine, widely agreed to be a sorely needed advance in care.

AI has the potential to help fix many of healthcare's biggest problems but we are still far from making this a reality. We can invent all the promising technologies and machine learning algorithms but without sufficient and well represented data, we cannot realize the full potential of AI in healthcare. Without these radical changes and collaboration in the healthcare industry, it would be challenging to achieve the true promise of AI to help human health.

Finally, our analysis will propose and discuss a dominant framework of variables in this field, and our analysis will not be limited to AI application descriptions. Using sophisticated deep learning frameworks and large-scale data analyses, AI is changing the healthcare industry. Significant and useful data may get lost in the massive data collection like a needle in a haystack, costing the industry billions of dollars annually. In addition, the creation of accurate diagnoses and new medications and medicines is slowed down without the ability to connect crucial data pieces. Statista reports that the AI healthcare market, which was valued at $11 billion in 2021, is expected to soar to $187 billion by 2030. This significant growth suggests that substantial transformations are anticipated in the operations of medical providers, hospitals, pharmaceutical and biotechnology companies, and other healthcare industry participants.

As we move towards a more connected digital world, the use of AI in the healthcare industry will become an invaluable asset that could change the way doctors treat patients and deliver care. With such great potential, it is clear that the applications of artificial intelligence in healthcare promises a future filled with advancements and better patient experiences. Advanced natural language processing is simply the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms.

Moreover, AI-powered decision support systems can provide real-time suggestions to healthcare providers, aiding diagnosis, and treatment decisions. Patients are evaluated in the ED with little information, and physicians frequently must weigh probabilities when risk stratifying and making decisions. Faster clinical data interpretation is crucial in ED to classify the seriousness of the situation and the need for immediate intervention. The risk of misdiagnosing patients is one of the most critical problems affecting medical practitioners and healthcare systems. A study found that diagnostic errors, particularly in patients who visit the ED, directly contribute to a greater mortality rate and a more extended hospital stay [32].

Artificial intelligence in medicine is the use of machine learning models to help process medical data and give medical professionals important insights, improving health outcomes and patient experiences. For example, radiographic systems and their outcomes (e.g., resolution) vary by provider. AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare. Artificial intelligence (AI) is becoming more common in modern industry and everyday life, and is increasingly used in healthcare.

Two IBM Watson Health clients recently found that with AI, they could reduce their number of medical code searches by more than 70%. In the President’s October AI Executive Order, he tasked  the Department of Health and Human Services (HHS) with a wide range of actions to advance safe, secure, and trustworthy AI. These actions include developing frameworks, policies, and potential regulatory actions for responsible AI deployment.

• According to the authors, intelligent machines raise issues of accountability, transparency, and permission, especially in automated communication with patients.
• These technologies can analyse raw data and provide helpful insights that can be used in patient treatments.
• AI technology can also be applied to rewrite patient education materials into different reading levels.
• The use of artificial intelligence in healthcare is widely used for clinical decision support to this day.
• This discussion guide identifies issues and key strategic questions leaders should consider to successfully integrate AI-powered technologies into their care delivery operations.

Coli, etc., at a far faster rate than they could with manual scanning thanks to AI enhanced microscopes. A number of healthcare companies have turned to AI in healthcare to stop the loss of data. They can now segment and connect the necessary data using AI, which used to take years to handle. As with most privacy issues, states are leading the way in the effort to protect individual privacy as AI use expands in healthcare. Currently, 10 states have AI-related regulations as part of their larger consumer privacy laws; however, only a handful of states have proposed legislation specific to the privacy of data or the use of AI in healthcare.

AI enables making fast decisions based on data, resulting in optimized allocation of resources. For instance, Notable Health offers an AI-driven project that automates administrative tasks in healthcare. It helps with registration and intake, scheduling, authorizations, referrals and billing. Binah.ai also pulls vital signs from a video of the upper cheek region of the face and studies this with advanced AI and deep learning algorithms, along with computer vision technology and signal processing. Virtual reality (VR) and augmented reality (AR) applications, driven by AI, offer immersive experiences that allow students to practice surgeries or diagnose patients virtually. These technologies provide a safe and risk-free environment for learning and honing medical skills.

The improved method aids healthcare specialists in making informed decisions for appendicitis diagnoses and treatment. Furthermore, the authors suggest that similar techniques can be utilized to analyze images of patients with appendicitis or even to detect infections such as COVID-19 using blood specimens or images [19]. Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. These journals deal mainly with healthcare, medical information systems, and applications such as cloud computing, machine learning, and AI.

Additionally, as this is a young research area, the analysis will be subject to recurrent obsolescence as multiple new research investigations are published. Finally, although bibliometric analysis has limited the subjectivity of the analysis [15], the verification of recurring themes could lead to different results by indicating areas of significant interest not listed here. In terms of practical implications, this paper aims to create a fruitful discussion with healthcare professionals and administrative staff on how AI can be at their service to increase work quality. Furthermore, this investigation offers a broad comprehension of bibliometric variables of AI techniques in healthcare. In doing so, we use a different database, Scopus, that is typically adopted in social sciences fields.

These robots augment the capabilities of healthcare professionals and improve patient outcomes in various healthcare settings. For example, automated transcription of medical records is a key application of NLP. Algorithms analyze spoken or written medical conversations, converting them into structured electronic formats. This saves time for healthcare professionals and facilitates efficient retrieval and analysis of patient information. For treatment optimization, algorithms analyze patient outcomes, treatment responses, and clinical guidelines to determine the most effective treatment options.