#AI tools in Healthcare

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The above image has been sourced from GAVS Technologies, AIOps Digital Transformation Solution provider.

To know more visit: https://www.gavstech.com/how-to-create-an-accessible-and-inclusive-design/

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Transform Text to Video Using Deep Brain AI Avatars!

Deep Brain AI is revolutionizing video creation by enabling users to transform text into lifelike videos effortlessly. With a diverse selection of over 100 realistic AI avatars representing various ages, ethnicities, and roles, we can create engaging content in just minutes.

This cutting-edge technology is especially advantageous for industries like finance, healthcare, and education that demand quick and captivating visual solutions. Discover how Deep Brain AI empowers us to enhance our storytelling capabilities while saving time and resources in the process.

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5 Challenges of AI in Healthcare

New Post has been published on https://thedigitalinsider.com/5-challenges-of-ai-in-healthcare/

5 Challenges of AI in Healthcare

Imagine a world where your smartwatch not only tracks your steps but also predicts a heart attack before it happens. It’s closer to reality than you think.

Artificial intelligence (AI) integration in healthcare has begun, unlocking many use cases for healthcare providers and patients. The AI healthcare software and hardware market is expected to surpass $34 billion by 2025 globally.

Among the technology and processes indicative of these investments in healthcare include:

Robotic nurses to aid surgeons.

Wearables for real-time health monitoring.

Medical AI chatbots for enhanced self-care.

Predictive diagnosis based on existing health symptoms.

However, these applications also come with complex challenges. This blog will explore the five challenges in implementing AI in healthcare, their solutions, and their benefits.

Challenges of Using AI in Healthcare

Physicians, doctors, nurses, and other healthcare providers face many challenges integrating AI into their workflows, from displacement of human labor to data quality issues.

1. Displacement of Human Employees

There is a growing concern that AI could replace healthcare professionals, including job displacement, an outdated skillset, and mental and financial hardships. This potential shift may deter medical groups from adopting AI, causing them to forego many benefits.

The challenge lies in balancing the integration of AI for routine tasks and retaining human expertise for complex patient care, where empathy and critical thinking are irreplaceable.

2. Ethical and Privacy Issues

Obtaining informed consent from patients on how AI systems will use their data can be complex, especially when the public does not fully understand the underlying logic. Some providers might also disregard ethics and use patient data without permission.

Additionally, biases in training data could result in unequal treatment suggestions or misdiagnosis. This discrepancy can disproportionately affect vulnerable groups.

For example, an algorithm that predicts which patients need more intensive care based on healthcare costs rather than actual illness. This incorrectly attributed a lower disease burden to black people.

Furthermore, AI’s ability to identify individuals through large amounts of genome data, even when personal identifiers are removed, poses a risk to patient confidentiality.

3. Lack of Digital Training and Adoption Barriers

A major problem is that medical students receive insufficient training on AI tools and theory. This unpreparedness makes adopting AI difficult during their internships and work.

Another significant barrier is the reluctance of some individuals to embrace digital technologies. Many people still prefer traditional, in-person consultations due to multiple reasons, such as:

The relatable nature of human interactions.

Uniqueness neglect by AI.

The higher perceived value of human doctors, etc.

This resistance is often compounded by a general lack of awareness about  AI and its potential benefits, particularly in developing countries.

4. Professional Liabilities

The use of AI systems in decision-making introduces new professional liabilities for healthcare providers, raising questions about ownership if AI initiatives are ineffective. For example, doctors can defer treatment plans to AI without taking responsibility for failed patient examinations.

Furthermore, while machine learning (ML) algorithms can offer personalized treatment recommendations, the lack of transparency in these algorithms complicates individual accountability.

Additionally, reliance on AI could lead to complacency among healthcare professionals, who might defer to computerized decisions without applying their clinical judgment.

5. Interoperability Problems and Data Quality Issues

Data from different sources can often fail to integrate seamlessly. Inconsistency in data formats across systems makes it difficult to access and process information efficiently, creating information silos.

Moreover, poor data quality—such as incomplete or inaccurate records—can lead to flawed AI analysis, ultimately compromising patient care.

Considering these challenges, how can healthcare organizations leverage the full potential of AI?

Solutions to Healthcare AI Problems

Solving the challenges introduced by AI involves a top-down approach. It begins with ensuring that data analysts thoroughly vet datasets used to train AI algorithms to eliminate biases and low-quality data. Transparency with patients regarding AI’s role in their treatment is also crucial to increase adoption.

An example is the Mayo Clinic, which used an algorithm that analyzed over 60,000 images to detect pre-cancerous signs. The algorithm’s accuracy was 91% compared to a human expert’s.

Apart from fixing old datasets, health regulatory bodies, such as the European Medicines Agency (EMA), must collect new, error-free data representing diverse populations to enhance accuracy. OpenAPS is an example of an initiative to create an inclusive open-source collection of systems to treat type 1 diabetes accurately.

Additionally, hospitals should enhance training and education for healthcare professionals. Educational authorities can also extend this specialized training to universities to prepare future practitioners.

This initiative will ensure familiarity with and expertise in AI tools and reduce resistance to their adoption in a professional setting. For example, Intuitive Surgical Ltd’s investment in the da Vinci system has helped doctors in over 5 million surgeries.

Investing in modern data integration tools, such as Astera and Fivetran, with built-in data quality features will also help. These tools remove siloed data and improve interoperability. They also enable data validation to ensure AI algorithms have clean data to analyze.

To effectively integrate AI systems into healthcare, medical institutions must balance leveraging AI and preserving human expertise. Adopting hybrid approaches like human-in-the-loop (HITL) models can help alleviate fears of job displacement. This approach will also ease patient concerns about AI involvement while allowing workers to improve productivity.

And, what are the benefits of successful AI integration within healthcare?

Benefits of AI in Healthcare

AI provides many benefits in the healthcare industry, including improved diagnosis and higher work efficiency:

1. Enhanced Diagnostic Accuracy

AI is transforming diagnostic processes by rapidly analyzing medical images, lab results, and patient data with remarkable precision. This ability to process large amounts of information quickly leads to early, potentially more accurate diagnoses, improving disease management.

2. Personalized Treatment Plans

AI-powered deep learning algorithms can process extensive datasets to create personalized treatment plans tailored to individual patients. This customization improves the efficacy of treatments and minimizes side effects by addressing each patient’s specific needs based on extensive sample data.

3. Operational Efficiency

By automating administrative tasks such as scheduling appointments and billing, AI allows healthcare providers to spend more time and effort on direct patient care. This shift reduces the burden of routine tasks, cuts costs, streamlines operations, and improves overall efficiency.

4. Improved Patient Monitoring

AI-powered tools, including wearable devices, offer continuous patient monitoring, providing real-time alerts and insights. For example, these devices can alert medical services in case of an unusually high heartbeat, which could indicate a physical injury or heart condition.

This proactive approach enables healthcare providers to respond swiftly to changes in a patient’s condition, improving disease management and overall patient care.

Looking Ahead

Emerging technologies, like virtual reality (VR) in medicine, will play a critical role. Many healthcare tasks, from diagnostics to treatment, will be AI-powered, enhancing access to care patient outcomes.

However, healthcare authorities must balance AI’s benefits and challenges to ensure ethical and effective integration into patient care. This will transform the healthcare delivery systems in the long term.

Explore Unite.ai for more resources on AI and healthcare.

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AI in Everyday Life: How Artificial Intelligence is Changing Our Dailys Routines

Introduction Artificial Intelligence (AI) is no longer a futuristic concept; it has become an integral part of our everyday life. From smart home devices to personalized recommendations on streaming services, AI is transforming how we interact with technology and manage our daily routines. This blog will explore the various ways in which AI is reshaping our lives, enhancing convenience, and improving efficiency.

Smart Home Technology One of the most visible impacts of AI in everyday life is the rise of smart home technology. Devices such as smart speakers, thermostats, and security systems utilize AI algorithms to learn our habits and preferences. For instance, smart thermostats can analyze your daily schedule and adjust the temperature accordingly, ensuring optimal comfort while saving energy. Similarly, smart speakers like Amazon Echo and Google Home can manage other connected devices, play music, answer questions, and even control your home’s lighting—all through simple voice commands.

Personalized Recommendations AI has revolutionized how we consume content through personalized recommendations. Streaming platforms like Netflix and Spotify use AI algorithms to analyze our viewing and listening habits, suggesting shows, movies, and playlists tailored to our tastes. This not only enhances our entertainment experience but also saves time by eliminating the need to sift through countless options. Similarly, e-commerce sites like Amazon employ AI to recommend products based on our browsing history and purchase patterns, making shopping more convenient and enjoyable.

Virtual Assistants Virtual assistants such as Siri, Google Assistant, and Cortana are prime examples of AI in everyday life. These tools help us manage our schedules, set reminders, and answer questions, all through voice commands. By integrating with our calendars and email accounts, virtual assistants can provide timely updates and reminders, ensuring that we stay organized and productive throughout the day. As these technologies continue to evolve, they are becoming increasingly adept at understanding context and providing more accurate responses.

Health and Fitness Tracking AI is also making significant strides in the realm of health and fitness. Wearable devices like fitness trackers and smartwatches use AI to monitor our activity levels, heart rate, and sleep patterns. By analyzing this data, these devices can provide personalized insights and recommendations to help us achieve our health goals. For example, AI-driven apps can suggest workout routines based on our fitness levels and preferences, making it easier for us to stay active and maintain a healthy lifestyle.

Enhanced Customer Service In the realm of customer service, AI chatbots are transforming how businesses interact with their customers. These intelligent systems can handle inquiries, provide support, and resolve issues 24/7, improving response times and customer satisfaction. By utilizing natural language processing, chatbots can understand and respond to a wide range of questions, allowing businesses to offer seamless support without the need for human intervention.

Conclusion In conclusion, AI in everyday life is changing how we live, work, and interact with technology. From smart home devices and personalized recommendations to virtual assistants and health tracking, AI is enhancing convenience and efficiency in our daily routines. As these technologies continue to advance, we can expect even more innovative applications that will further integrate AI into our lives. Embracing these changes will not only improve our quality of life but also pave the way for a smarter, more connected future. As we move forward, it is essential to understand and harness the power of AI to enhance our everyday experiences while remaining mindful of its implications.

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How to navigate the world safely with AI

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Generative AI has introduced medical chatbots, which offer patients personalized medical attention and advice when required. For example, a company has developed a generative AI medical chatbot. Now, the chatbot will ask patients about their problems, underlying symptoms, past medical history, and more to deliver personalized plans and medical care.  

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Siri, Alexa, My Privacy is Calling: Unveiling the AI Assistants in Our Pockets

They whisper from our phones, chime from our smart speakers, and even control our thermostats from afar. No, we're not talking about benevolent ghosts, but the ever-evolving world of AI personal assistants, those digital companions nestled snugly in our pockets and homes. But while these tech marvels make our lives undeniably easier, a whisper of concern echoes in the background: are we sacrificing our privacy for convenience?

Let's face it, these AI assistants are voracious data devourers. Every "Hey Siri" or "Alexa, play Beethoven" fuels their algorithmic engines, building detailed profiles of our habits, preferences, and even location data. While this personalized experience is undeniably cool (imagine a playlist crafted for your exact mood!), it paints a worrying picture: a digital map of our lives, laid bare in the silicon hearts of these assistants.

The concerns are real. Security breaches, targeted advertising, and even potential government access to this data are no longer dystopian fiction. This raises some crucial questions:

Who owns our data? Who controls how it's used and for what purposes? Are we truly informed about the extent of data collection? Do we fully understand the hidden costs of whispering commands to our digital helpers? How can we strike a balance between convenience and privacy? Can we enjoy the benefits of AI assistants without feeling like Big Brother is eavesdropping on our grocery lists? The good news is, we're not powerless. We can be proactive guardians of our digital selves. Here are some practical steps we can take:

Read the privacy policies! Yes, they're long and dry, but understanding how your data is used is crucial. Adjust your privacy settings. Most AI assistants offer some level of control over data collection and use. Take advantage of them! Think before you speak. Do you need to tell your assistant your address to order pizza? Sometimes, the old-fashioned phone call is best. Demand transparency and accountability. Let tech companies know we value our privacy and expect responsible data handling. Ultimately, AI personal assistants are powerful tools, capable of enriching our lives. But like any tool, they require responsible use and careful consideration. Let's not surrender our privacy in exchange for convenience. Instead, let's be informed, discerning users, navigating the exciting world of AI with our eyes wide open (and our voices whispering responsibly).

Together, we can ensure that the future of AI assistants is one where convenience and privacy dance in perfect harmony, without Big Brother ever getting invited to the party.

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Top Generative AI Use Cases for Healthcare to Enhance Patient Experience.

Exciting news! Discover how Generative AI is transforming healthcare and enhancing patient experience. From personalized treatment plans to virtual health assistants, this blog post explores the top use cases and practical applications. 🌟 Find out more here: https://ift.tt/5FKOzrH #AIinHealthcare #PatientExperience List of Useful Links: AI Scrum Bot - ask about AI scrum and agile Our Telegram @itinai Twitter -  @itinaicom

[gavstech]

NLP is becoming a valuable AI tool in healthcare analytics, specifically for identifying keywords in medical records. There are many other potential applications that remain unexplored. NLP is gaining traction in organizations, unlocking countless possibilities for its use in the future.

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Paul Roscoe, Chief Executive Officer, CLEW Medical – Interview Series

New Post has been published on https://thedigitalinsider.com/paul-roscoe-chief-executive-officer-clew-medical-interview-series/

Paul Roscoe, Chief Executive Officer, CLEW Medical – Interview Series

Paul Roscoe is the Chief Executive Officer of CLEW Medical.

Prior to joining Clew, Mr Roscoe was CEO of Trinda Health, and was responsible for establishing the company as the industry leader in quality oriented clinical documentation solutions.

CLEW Medical offers hospitals, healthcare systems and intensive care units advanced clinical intelligence and patient diagnostics using AI-powered, FDA-cleared predictive analytics and proprietary critical care models.

Could you start by telling us a bit more about CLEW Medical’s AI-enabled platform and its unique capabilities in the MedTech industry?

CLEW’s founding was based on the premise that data analytics and AI can significantly improve patient outcomes and clinician experience in high-acuity care settings. The clinical surveillance platform we’ve built is the first to have FDA-cleared AI-driven prediction models for critical care. Our system obtains data by integrating with all clinical data sources within a hospital and builds a near real-time physiological profile of each patient to continuously monitor their status. It then uses this data to provide predictive insights to identify patients who will likely have an adverse event – such as respiratory failure – and alert clinicians to intervene up to eight hours before the anticipated event. The platform’s high degree of accuracy also reduces the excessive number of false alarms, enabling clinicians to practice at the top of their license and focus on patients most in need of immediate intervention.

What were the key factors that contributed to the FDA clearance of CLEW’s AI-driven predictive models?

CLEW has embraced AI since its inception. Our founders and developmental leaders recognized the significance of fostering trust with caregivers, the individuals responsible for utilizing our technology to care for their most vulnerable patients. It was imperative that our technology undergo the same level of scrutiny and diligence in design, development, testing, and validation as the devices already in use by our users. To encourage the adoption of an AI solution for critical care settings, our team understood the necessity of building models with meticulous product development and quality systems. As a result, our AI model development leverages robust MLOPS (machine learning operations) infrastructure to meet regulatory expectations, such as the PCCP (pre-authorized change control plan) guidance from the FDA. Our AI models are methodically designed, while undergoing all necessary experiments for medical device regulatory clearance.

The robustness of the models and our internal processes resulted in the FDA classifying our solution as a class II medical device in early 2021, which exemplified a landmark, first-of-its-kind achievement. FDA medical device clearance serves as a testament to the quality of our end-to-end development process, which includes clinical validation studies conducted in real patient populations.

The recent study published in CHEST® Journal highlighted the predictive accuracy of your AI models. Can you discuss the methodology and the specific findings of this study?

A CLEW-trained ML algorithm was deployed in 14 intensive care units (ICUs) across two major health systems to predict intubation and vasopressor initiation events – in other words, events that require life-saving intervention – among critically ill adult patients. Its performance was measured against existing bedside monitoring alarms and the predictive effectiveness of telemedicine system alerts.

The study, designed to evaluate the tool’s accuracy and utility of alerts in ICUs, found that CLEW’s models for predicting patient deterioration were five times more accurate than and produced 50 times fewer alarms than the leading telemedicine system. The findings also show that the ML model has superior accuracy compared to traditional monitoring systems and drastically reduces unnecessary interruptions to clinician workflows.

How do the AI predictions made by CLEW’s platform potentially transform care delivery in the ICU? Could you elaborate on how these predictions improve outcomes and reduce complications?

CLEW’s platform produces opportunities for early interventions in high-risk patients and supports capacity management by identifying low-risk individuals who may be ready for step-down or discharge. This, in turn, decreases mortality and readmission rates, reduces complications caused by patient deterioration, and minimizes patients’ length of stay.

For example, within the first 24 hours of deployment at a major health system, our technology predicted hemodynamic instability in an ICU patient, which triggered a provider evaluation. Upon evaluating the patient, the provider ordered a CT scan and detected an abdominal bleed. The patient was rushed to the operating room for emergency surgery, infused with fluids and blood, and their life was ultimately saved. 24 hours later the patient was in stable condition.

Your system was found to be five times more accurate than a leading telemedicine monitoring system. What makes CLEW’s technology more effective in predicting critical patient deteriorations?

In general, ML-generated notifications are less frequent, have higher levels of accuracy and lower rates of errors such as false positives, and create longer pre-event lead times than other telemedicine system alerts and bedside monitoring system alarms. CLEW’s alerts are more accurate and functional and provide time for the care team to adopt countermeasures to prevent predicted outcomes. The sophisticated intelligence that CLEW provides is made possible by its ability to mine patient data from a health system’s electronic medical record (EMR), combined with ML models that have been rigorously tested and validated through peer-reviewed research and FDA clearance.

The study also noted a significant reduction in false alarms. How does reducing alarm fatigue benefit ICU staff, and what has been the feedback from healthcare professionals using your system?

98% of bedside monitoring notifications are false positives, leading to alarm fatigue and exacerbating historically high levels of clinician burnout. CLEW addresses alarm fatigue by reducing the number of auditory interruptions, increasing the percentage of actionable notifications for necessary provider intervention, and creating an overall calmer ICU environment. In essence, the platform’s accuracy and ability to reduce unnecessary workload via advanced ML models significantly improves ICU burnout. As part of the implementation process, CLEW’s customer success teams focus on the importance of clinical change management to ensure the technology is appropriately incorporated into the overall clinical decision-making process. The feedback from clinicians has been extremely positive.

How does the early notification feature of CLEW’s platform work, and what kind of interventions has it facilitated in real-world ICU settings?

Based on the incoming stream of information from bedside monitoring and life-support devices, as well as from the Electronic Health Record (EHR), the CLEW AI models can make predictions about the risk of patient deterioration and death over the next eight hours. With these predictive assessments, experienced clinicians can evaluate patients more closely and determine if there are applicable countermeasures to prevent the predicted deteriorations, instead of responding to them on an emergency basis.

For example, the CLEW platform can notify clinicians that a patient is highly likely to enter respiratory failure, which typically leads to intubation and mechanical ventilation. Upon receiving the alert, caregivers can then identify the patient has an excess of fluid that could start backing up into the lungs, and initiate diuretic therapy to reduce the fluids, thus preventing an intubation later. Our model can also anticipate whether a post-surgical patient is likely to become hemodynamically unstable and require vasoactive medication support. Armed with this knowledge in the absence of obvious symptoms, a CT-scan determined the patient had internal bleeding and was taken back to surgery to repair it. Ultimately, this intervention resulted in the patient being stabilized.

CLEW’s AI-enabled predictions also support hospitals with capacity management needs. Some patients will no longer require critical care and can be transferred to lower-acuity care units, freeing up beds to manage more critically ill patients. This allows the health system to improve capacity management and create access for more patients. This also increases contribution margin for the health system.

What are the next steps for CLEW Medical in terms of further developing and expanding the use of your AI-driven models in different healthcare settings?

We have already expanded the CLEW platform outside of critical care settings to include step-down units and emergency departments, and we are currently in the process of expanding across the remaining acute care beds of hospitals, including post-anesthesia care units (PACU) and general medical/surgical & specialty beds. The eventual ubiquity of inexpensive wearable monitors providing frequent vital signs information, along with our PCCP clearance, enables CLEW to expand its AI surveillance capabilities more broadly throughout acute care hospitals.

Additionally, as CLEW predictions are complementary to many other HIT systems including the EHR, we are working on delivering our insights via integration into a health system’s existing toolkit.  We have joined the Epic developers’ network and have demonstrated successful integration of advanced CLEW capabilities such as AI-driven predictions into the clinical user experience.

CLEW is also embarking on a novel, AI-driven approach to sepsis management, a devastating and sometimes deadly complication.

Where do you see the future of AI in improving ICU care over the next decade, and how does CLEW plan to be a part of this future?

Hospital patient populations are sicker than they used to be. With increasing age and lifestyle-related chronic illnesses alongside widespread caregiver shortages, the need for intelligent clinical surveillance continues to grow. Since many patients end up in ICUs because of missed opportunities to intervene earlier in the care process, CLEW is not only focused on using its AI to improve ICU care, but also on partnering with health system and industry innovators to improve all acute care. Our programmatic pipeline for AI development (MLOPS) will harness partner capabilities to grow FDA-cleared AI models beyond what CLEW develops on its own.

However, technology is only a part of solution. The use of AI in healthcare is not about replacing caregivers. In fact, AI can offer superior information to support their decision making to provide optimal clinical care, such as reducing noisy alerts that waste their time. CLEW is working with health systems and partners to learn from and educate caregivers on how AI tools can be effectively adopted and accepted into clinical practice. Research that validates the accuracy and efficacy of AI is required, so CLEW works with its customers to generate this proof with their own patient populations. This focused research effort supports implementation and adoption by bedside caregivers who would otherwise be skeptical.

To expedite new clinical implementations, we have the ability to update our platform to include newly discovered best practices within a month, something that typically takes years. Over the next decade, CLEW will be at the forefront of working with health systems to make effective clinical AI the informed and prescient partner of the human caregivers who may someday care for us or our loved ones.

Thank you for the great interview, readers who wish to learn more should visit CLEW Medical.

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