Category: Healthtech

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Applied Innovation Healthtech

How Artificial Intelligence can help identify Melanoma

How Artificial Intelligence (AI) is aiding in the diagnosis of skin cancer; Image Courtesy: Pixabay
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Applied Innovation Healthtech

How Artificial Intelligence can help identify Melanoma

Every area of healthcare is being significantly impacted by artificial intelligence (AI), and dermatology is no exception. Melanoma identification using AI is one possible application for AI in dermatology. Melanoma is the deadliest type of skin cancer and is difficult to detect and can be fatal. Artificial intelligence (AI) in this context can identify melanoma with a high degree of precision. This is crucial because the number of skin biopsies is increasing while the number of pathologists is decreasing leading to slows down in the rate of identification and, consequently, therapy.

The Process

The process includes the use of Deep Learning to build Convolutional Neural Networks (CNNs), a subcategory of machine learning. CNNs are a form of network architecture for deep learning algorithms and are specifically used for image recognition and other tasks requiring the processing of pixel data. They are therefore perfect for positions requiring computer vision (CV) skills as well as situations requiring precise object detection.

Data collection is the first step in dermatology scans for melanoma, where a sizable dataset of pictures of moles, lesions, and other skin anomalies is gathered and annotated by doctors to build a training set. The machine learning programs’ training on this information comes next during which, the system learns to recognize the characteristics of a melanoma lesion and distinguish them from other kinds of skin anomalies.

After the system is trained it is then incorporated into a dermatologist’s workflow. The dermatologist would capture photos of any suspicious lesions during a skin examination and upload them to the AI system, which would then evaluate the pictures and offer a diagnosis. A possible melanoma lesion would be flagged by the algorithm, prompting the physician to conduct additional testing.

After reviewing the image and the AI-generated analysis, a dermatologist may use additional diagnostic techniques like biopsy to support or contradict the prognosis. In order to increase the precision of the system, dermatologist comments on how well the AI system performed is integrated back into the training data.

An artificial intelligence (AI) system hence helps medical workers in developing possibly successful treatments and improving patient results. It can also increase access to treatment and raise the number of patients who can be seen and diagnosed quickly.

Conclusion

Dermatologists are now outperformed by artificial intelligence (AI) in the diagnosis of skin cancer, but dermatology is still lagging behind radiology in its widespread acceptance. Applications for AI are becoming easier to create and use.

Complex use cases, however, might still necessitate specialist knowledge for implementation and design. In dermatology, AI has a wide range of uses including basic study, diagnosis, treatments, and cosmetic dermatology.

The main obstacles preventing the acceptance of AI are the absence of picture standardization and privacy issues. Dermatologists are crucial to the standardization of data collection, the curation of data for machine learning, the clinical validation of AI solutions, and eventually the adoption of this paradigm change that is transforming our practice.

We want to make innovation accessible from a functional standpoint and encourage your remarks. If you have inquiries about evolving use cases across various domains or want to share your views email us at open-innovator@quotients.com

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Applied Innovation Healthtech

How Healthcare Can Benefit From Data Lakes

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Applied Innovation Healthtech

How Healthcare Can Benefit From Data Lakes

Healthcare companies are racing against the clock to improve the efficiency of their electronic health data (EHR). As a consequence, healthcare providers need to create new analytical models in order to spot at-risk patients, avoid adverse events, and practice evidence-based medicine. The emergence of cutting-edge research and forecasting models has led healthcare organizations to use data to verify theories and improve them using these techniques.

Data-informed Healthcare Organisations

The boom in AI and machine learning capabilities, and the accessibility of high-performance, storage-efficient hardware via the Commercial Cloud have all contributed to the emergence of the data-informed healthcare organization. The entrance hurdle for data-informed intelligence has decreased as a result of the market’s recent influx of technology and talent. This has ushered in a decrease in expense and improved the learning curve propelled by market and innovation.

Having a solid data infrastructure is the first step toward becoming a data-informed healthcare company. The information must be safe while still being easily accessible to those who require it. Systems must be able to search through the data in a matter of seconds or less, also the data must be inexpensive to keep in extremely large quantities. Complex data such as JS Object Notation or pictures. must be available through common query languages like SQL.

Enter the Healthcare Data Lake, a compilation of datasets that includes clinical data from electronic health record systems, societal factors of health data, analytical output from quality assessment and risk adjustment programs, and patient claims history.

Understanding Data Lakes

When compared to the clean, processed data kept in conventional data warehouse systems, a data lake’s ad hoc character is indicated by the word “data lake.”

A data lake is a gathering of different data assets that are kept within a Hadoop ecosystem with little alteration to the original format or substance of the source data. It is not just Big Data. As a result, the data lake does not have an explicit schema-on-write. Several programs that use “schema-on-read” are used to access the data lake’s information.

Big data from numerous sources are kept in a raw, granular version in a data lake, which is a primary storage location. Data can be retained in a more flexible shape for future use because it can be stored in an organized, semi-structured, or unstructured manner. A data lake identifies the data it stores with IDs and metadata markers to speed up retrieval.

Leveraging Data Lake for Healthcare

The data lake produces one complete, consolidated source of data for healthcare companies by removing the barriers posed by siloed data sources in various forms. This must be accessible on-demand to aid a motley of clinical and business use cases.

Healthcare organizations and health plans are considering whether their general data design needs a corporate data repository, a data lake, or both.

Some businesses seek to address complicated problems with a combination of alternative and conventional implementations, and there is a market change towards mixed approaches to data management.

The challenge facing healthcare leaders is to drive member and patient involvement, enhance patient medical results, and reduce the cost curve. These organizations must have the capacity to quickly ingest and evaluate sizable quantities of data in batch or in real-time from a wide range of sources in a variety of forms in order to accomplish this.

Benefits of Data Lake for Healthcare

Healthcare companies can enhance their data with data lakes to obtain more complete, useful insights to drive therapeutic and business efforts. For example, it can assist healthcare companies to leverage clinical data in order to find populations or diagnoses that may be under-reported for risk and quality initiatives. Additionally, it can provide access to real-time clinical data to care managers, allowing them to effectively prevent unnecessary emergency room visits, hospitalizations, and so on.

Data lakes can also aid in the integration of useful clinical results into provider report cards, as well as the tracking of opiate prescription trends to spot potential patient safety concerns and discover instances of fraud, waste, and abuse. Benefit design, network, and quality efforts can all gain from evaluating member care-seeking trends using data lakes.

We have innovators working to expedite data-centric techniques which enable biopharma and medical technology firms to provide cost-effective treatments to patients more quickly. Please contact us at open-innovator@quotients.com if you want to learn more about such solutions.

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Applied Innovation Healthtech

Improving Healthcare with Clinical Data Intelligence

UK government funding innovative research on artificial intelligence (AI) to advance healthcare
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Applied Innovation Healthtech

Improving Healthcare with Clinical Data Intelligence

Clinical Data Intelligence for Life Sciences solutions is making data gathering and categorization effective and intelligent, lowering mistakes and speeding up submissions.

In the age of machine learning, artificial intelligence, and semantic data pools, no nugget of information is wasted. The healthcare sector has advanced significantly in clinical decision support and predictive analytics in just the last few years.

As Data are becoming more accessible in the healthcare sector as opposed to a siloed strategy. The use of technology and data and data-driven value creation is now being witnessed throughout the network. Healthcare organizations now have the chance to better leverage data, improve patient care, and increase revenue while handling increasing risks in patient privacy and data security as new data technologies with advanced intelligence capabilities become available.

With businesses investing more in population health management and accountable care, the use cases for big data are multiplying quickly, and consumers are keeping up with the demand for affordable services that take advantage of the ease of their preferred applications and devices. This results in better treatment outcomes, individualized care, and preventive interventions. We would be discussing some of the emerging use cases going forward.

Preventive Healthcare: Preventive Healthcare is one of the use cases for clinical data intelligence. It enables experts to identify dangers early and take preventative measures. Through the use of data science techniques like AI and machine learning, wearables and other tracking devices that gather and track data are producing forecast models that can correctly identify a person’s health risks and enable carers to take preemptive action. It is feasible to anticipate and avoid chronic cardiac conditions, autism meltdowns, etc. by utilizing genetic and historical data.

Data-Driven Care: Data science technologies can make uses like medical image analysis and pathology reports that read with high precision possible because a large number of patients perish each year as a result of diagnostic mistakes. To analyze and understand X-rays, MRIs, mammograms, and other imaging studies, as well as to spot trends and identify illnesses, data models and algorithms can be created. This will increase output and aid doctors in making correct diagnoses.

Individualized Care: A one-size-fits-all strategy for medical care and medication is also now considered ineffective. The ability to monitor individual data and improvements in gene technology are enabling customized medicine. Based on a patient’s prior medical history, gene markup, and current data, machine learning, and deep learning algorithms can now help a doctor determine whether a specific drug will be effective for the patient.

Lowering Costs: Insurance firms are putting weight on healthcare organizations to improve therapy outcomes in order to lower readmissions. Longer personal care is a consequence of bed shortages in some nations. By enabling doctors to remotely monitor their patient’s vital signs, receive alerts when conditions deteriorate, and take appropriate action when required, data science and intelligence can significantly assist in resolving these problems.

Drug discovery: Drug development and clinical studies are lengthy, expensive procedures. Data intelligence tools can aid scholars in the analysis of huge data sets and in the creation of computer models for various tests. Additionally, text mining can assist medical academics by automatically reviewing thousands of web resources and performing analytical processing quickly to give the required information. Clinical trials will use data science apps to accelerate findings and reduce costs.

Thus providing companies with cutting-edge capabilities can improve care, accomplish improved treatment outcomes, increase patient experience, and make new discoveries in drug discovery, data science, and intelligence will have a major influence on the future of the healthcare sector.

However, healthcare companies are still having trouble mastering descriptive analytics, particularly when valuable insights call for a variety of data sources. Despite the data-driven promises, the majority of healthcare companies still have a lot of work to do before they can turn their growing big data analytics skills into actually usable clinical information.

Are you interested in implementing data science and intelligence in your company? Quotients through its partner networks offers a quicker, more affordable alternative. Using advanced data science technologies like AI, machine learning, deep learning, etc. without the constraints of time, money, and resources is made possible by our solutions. Please write to us at open-innovator@quotients.com

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Applied Innovation Healthtech

Machine Learning Model Accelerates Antibody Therapy Development

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Applied Innovation Healthtech

Machine Learning Model Accelerates Antibody Therapy Development

Therapeutic antibodies

Therapeutic antibodies are currently a popular type of medication having great efficacy and few negative effects. These are biopharmaceuticals that are designed to elicit a biological response.

These medications make use of antibodies, which are key participants in our body’s immune system. Individual assaults on specific antigens are feasible by leveraging the specificity of each antibody, which detects just one antigen. It isn’t easy to create and optimize therapeutic antibodies. Once an antibody that binds to the proper antigen is found, it goes through a time-consuming and resource-intensive optimization procedure.

Recently, computational techniques for dealing with such challenges have begun to follow machine learning paradigms, notably deep learning in many cases. This paradigm change improves known domains like structure or binding prediction while also opening up new possibilities like language-based modeling of antibody repertoires or machine-learning-based synthesis of novel sequences.

Machine Learning Algorithm aid Antibody Therapy Development

Researchers have now created a machine learning algorithm to aid in the optimization phase of antibody therapy development.
A few thousand therapeutic candidates can be tested in a lab using automated techniques. Machine learning has the potential to boost the first set of antibodies to be tested by millions. The more candidates there are to pick from, the more likely one will fit all of the requirements for medication development.

AI-enabled antibody design platform

We have innovators that develop, more effective antibody therapeutics for patients by combining machine intelligence and synthetic biology to create safer. We critically explore recent advances in (deep) machine learning techniques to therapeutic antibody design, with implications for completely computational antibody creation, in this review. Our AI-enabled antibody design platform provides the necessary technology to rapidly and reliably develop these game-changing medicines.

In each cycle, our machine learning algorithms generate hundreds of variations that are created and evaluated in our lab utilizing the most advanced synthetic biology technology. The biophysical features and influence on disease activity of each mutation are measured using cell-based or other functional tests that reproduce in vivo disease processes.

This fresh data is utilized to upgrade the AI/ML models so that these models learn to manufacture antibodies that fit our design blueprint across numerous cycles. For additional information on this solution as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com.

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Applied Innovation Healthtech

Digital Surgery Platform that makes surgery smarter and safer

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Applied Innovation Healthtech

Digital Surgery Platform that makes surgery smarter and safer

COVID-19 has accelerated the implementation of digital technology and procedures in healthcare, a positive development for patients, clinicians, and health systems alike. One of many areas impacted is the surgical procedures which used to remain fragmented and where most of the innovation occurred in silos that frequently did not interact or link effectively enough with one another.

But medical treatments are now achieving health results that were previously unattainable due to the invasive nature of conventional surgery, without jeopardizing the patient’s recovery, thanks to improved, connected, more intuitive, and efficient care made possible by revolutionary digital instruments.

Digital Surgery

Robotics, virtual and augmented reality, and artificial intelligence (AI) all hold the potential of data-driven precision surgery, with the ultimate objective of enhancing patient outcomes, surgical performance, and the productivity and efficiency of surgeons and their teams.

The definition of digital surgery as the use of technology to improve preoperative planning, surgical performance, therapeutic assistance, or training in order to improve outcomes and decrease damage received unanimous approval.

Digital technology use is not restricted to the operating room; it currently plays a role in areas as diverse as preoperative planning, surgical risk prediction, and surgical performance assessment. Commercial potential and the promise of better results for surgeons and patients are driving the rapid adoption of these technologies.

Digital Surgical Platform

We offer a digital surgery platform that provides actionable insights to make surgery smarter and safer. The digital surgery platform analyses massive amounts of real-world data in and around the operating room (OR) using proprietary software and artificial intelligence (AI).

This real-world evidence may be used by the care team in real-time during surgery and viewed by others outside the operating room via the platform’s dedicated telehealth link. Following a procedure, the platform provides insights that assist surgeons in benchmarking and improving their care, hospital administrators in making better use of surgical resources, medical device companies in developing better products, and insurance companies in understanding risk and developing more tailored policies.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

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Federated Learning for Medical Research

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Applied Innovation Healthtech

Federated Learning for Medical Research

Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial Intelligence (XAI) have emerged as the most popular and fascinating technologies in the intelligent healthcare industry.

The traditional healthcare system is centered on centralized agents providing raw data. As a result, this system still has significant risks and problems. When combined with AI, the system would consist of several agent collaborators capable of successfully connecting with their intended host.

Federated Learning, a novel distributed interactive AI paradigm, holds promise for smart healthcare since it allows several clients (such as hospitals) to engage in AI training while ensuring data privacy. FL’s noteworthy characteristic is that operates decentralized; it maintains communication based on a model in the selected system without exchanging raw data.

The combination of FL, AI, and XAI approaches has the potential to reduce the number of restrictions and issues in the healthcare system. As a consequence, the use of FL in smart healthcare might speed up medical research using AI while maintaining privacy.

The Federated Learning approach may be used to provide several enticing benefits in the development of smart healthcare. Local data, for example, are not necessary for training. To train other machine learning algorithms by mixing a large number of local datasets without transmitting data. During training, local Machine Learning (ML) models are trained on local heterogeneous datasets.

When opposed to traditional centralized learning, FL is also capable of delivering a good balance of precision and utility, as well as privacy enhancement. FL may also help to reduce communication costs, such as data latency and power transmission, connected with raw data transfer by avoiding the dumping of huge data quantities to the server.

We have solutions that use FL to link life science enterprises with world-class university academics and hospitals in order to exchange deep medical insights for drug discovery and development. The platform enables its partners to uncover siloed datasets while maintaining patient privacy and securing proprietary data by leveraging federated learning and cutting-edge collaborative AI technologies. This enables unprecedented cooperation to enhance patient outcomes by sharing high-value knowledge.

The platform has built a worldwide research network driven by federated learning, allowing data scientists to securely connect to decentralized, multi-party data sets and train AI models without the need for data pooling. When combined with fields of medicine specializing in diagnosis and treatment, scientists may use cutting-edge technology platforms to build potentially life-changing drugs for people all over the world.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

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Artificial intelligence revolutionizing drug discovery and development

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Applied Innovation Healthtech

Artificial intelligence revolutionizing drug discovery and development

Incredible medical discoveries are revolutionizing our ability to treat and even cure patients; but, drug discovery and development is becoming more difficult and costly, leaving many patients without viable medicines.

Simultaneously, throughout the last decade, a revolution in machine learning has enabled answers to issues that were formerly deemed intractable. Machine learning approaches can currently caption photos, translate across languages, and identify voices at or above human performance levels.

One of the systems utilized in AI is neural networks, which may be used to identify chemical structures with medicinal significance. A neural network uses a large set of training data containing information about the chemical structure-biological activity relationship, which is preceded by successful neural network training and acquisition of relevant information about chemical compounds, functional groups, and their possible biological activity.

The data is derived through experimental observations as well as from relevant quantum models. There were constraints in biological data a few years ago – while access to huge, rich data sets has spurred machine learning’s development, such data sets are still rare in biology, where data collection remains essentially artisanal. Recent advances in cell biology and bioengineering are now allowing us to change this by facilitating the generation of huge volumes of biological data. Besides, researchers have revealed that neural networks have a substantial capacity to create generalizations based on even very restricted training data.

Pipelines for drug discovery and development are lengthy, complicated, and dependent on a variety of factors. Machine learning (ML) techniques offer a collection of tools that can enhance discovery and decision-making for well-specified queries with a large amount of high-quality data. Opportunities to use ML arise at various phases of drug development.

Instead of depending on restricted “discovered” data, we have solutions that use contemporary biology technologies to build high-quality, huge data sets designed for machine learning, allowing us to unleash the full power of modern computational methodologies.

Our solutions are created by professional biologists and drug hunters who collaborate with cutting-edge technologists and machine learners. A group of life scientists and data scientists, software engineers, process engineers, bioengineers, translational scientists, and drug hunters are collaborating to answer problems that we would never have thought to ask on our own.

For additional information on such solutions and emerging use cases in other areas, as well as cooperation and partnership opportunities, please contact us at open-innovator@quotients.com

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Healthtech Applied Innovation

AI solution for the screening of lung cancer and other respiratory disorders

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Healthtech Applied Innovation

AI solution for the screening of lung cancer and other respiratory disorders

Lung Cancer: 2nd most common cancer worldwide

Smoking is considered to be responsible for around 80% of lung cancer fatalities, and this figure is likely to be much higher in the case of small cell lung cancer (SCLC). By far the most significant risk factor for lung cancer is smoking.

Persons who smoke have a far higher chance of developing lung cancer than people who do not smoke. The longer you smoke and the more packs you smoke daily the higher your risk.

A risk factor is something that enhances a person’s likelihood of contracting an illness such as cancer. Risk factors for various malignancies vary. Some risk factors, such as smoking, can be modified. Others, such as a person’s age or family history, are unchangeable.

However, possessing a risk factor, or even several, does not guarantee that you will get the illness. Furthermore, some persons who have the condition may have little or no established risk factors.

Lung cancer is the 2nd most common cancer worldwide, according to WCRF. It is the most common cancer in men and the 2nd most common cancer in women. There were more than 2.2 million new cases of lung cancer in 2020.

Lung Cancer Screening

According to a study, the risk category for lung cancer has spread to a younger generation, whether smokers or nonsmokers. The WHO has identified air pollution as a critical contributor to the growing trend of young, nonsmokers being at risk for lung illnesses.

Some experts label air pollution a ‘hidden public health emergency’ and ‘the new tobacco’. It offers a new problem for doctors attempting to treat and prevent lung cancer: determining risk factors for the illness.

Simply put, how does one tackle the risk of lung cancer in a 25-year-old, non-smoking individual living a reasonably healthy lifestyle when a risk factor could be the simple act of breathing?

Lung cancer screening is a procedure that detects the existence of lung cancer in otherwise healthy persons who are at high risk of developing lung cancer. To detect lung cancer, doctors utilize a low-dose computerized tomography (LDCT) scan of the lungs. Lung cancer is more likely to be treated if it is diagnosed at an early stage.

Artificial Intelligence (AI) solution for Screening

At various degrees of detection difficulty, artificial intelligence (AI) can enhance the identification of pulmonary nodules on chest radiographs. When compared to the unaided interpretation of chest x-rays, AI-aided interpretation can dramatically enhance the identification of pulmonary nodules.

On imaging, most early lung malignancies appear as pulmonary nodules, which can be readily overlooked on chest radiographs. When compared to unaided interpretation, an AI system may enhance the diagnostic performance of radiologists with varying degrees of expertise in recognizing pulmonary nodules on chest radiographs.

We have an artificial intelligence (AI) solution for chest X-rays that have been tried, tested, and trusted to aid in the identification and reporting of missing nodules, emphasizing the need for opportunistic screening for detecting probable lung malignancies early.

Our solution is backed by research that has found that AI-based systems might better detect key discoveries on Chest X-Rays, such as cancerous nodules. This raises the prospect of opportunistic screening for lung cancer and other respiratory disorders becoming the norm.

These solutions may genuinely make a difference, supplementing physicians’ and radiologists’ efforts every time a Chest X-ray or Chest CT is performed.

To know more about such innovative solutions and evolving use cases in different domains along with collaboration and partnership opportunities please write us at open-innovator@quotients.com

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Healthtech Applied Innovation

AI-based Voice Assistants for Healthcare Industry

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Healthtech Applied Innovation

AI-based Voice Assistants for Healthcare Industry

Voice AI can play a great role in the healthcare industry, transforming day-to-day operations for healthcare providers, and patients, and ensuring compliance. With the advanced algorithm of machine learning and natural language processing, they can help in preventing physician burnout, eliminating transcription delays, reducing documentation, and enabling healthcare providers to concentrate on patient care.

Historically, a personal assistant or secretary would handle activities including taking dictation, reading aloud text or email messages, searching up phone numbers, scheduling, making phone calls and reminding the user of upcoming appointments. Currently, popular virtual assistants including Amazon Alexa, Apple Siri, Google Assistant, and Cortana, carry out the user’s requests. This application software known as a virtual assistant can recognize natural language voice commands and performs tasks according to user requests.

Voice assistants can provide efficiencies that lessen effort and enhance care. Automation of repetitive operations, the removal of data stream obstacles, and a direct reduction in the time and effort required by care groups can be made possible by AI-powered voice assistants. Despite appearing in healthcare records and other databases, patient information is rarely captured throughout the patient journey. AI-powered patient insights with omnichannel voice insight, in this case, may also be used to generate value for the patient and the practitioner, whether it be through symptom checks, providing high-quality treatment, or enhancing outcomes.

One of the challenges for AI-powered voice assistants is the complex medical vocabulary, but there are solutions that are now offering better ergonomic data entry with good accuracy and robust medical vocabularies. Another challenge is that it is difficult to find the necessary datasets to train AI models for these kinds of jobs as the type of data utilized has a significant impact on AI. There could be restrictions on the tones, accents, and languages that these algorithms can comprehend in the early stages. But there are solutions available that do not need voice profile training, from any device, anywhere.

With easy-to-use features, these solutions can work with different clinical systems and maintain electronic health records, etc. The privacy issue is another factor that may be a cause of concern as it collects data, particularly biometric data like voice data. But the solutions are available that are HIPAA Compliant, GDPR Compliant and other with certifications that take care of these concerns. With easy-to-use features, these solutions can work with different clinical systems and maintain electronic health records, etc.

New tools and technologies are already starting to make waves across the healthcare system but the industry in general has been a slow adopter of these changes. Emerging technologies like genome sequencing, digital tools, and artificial intelligence (AI) hold great promise to transform the delivery of health services in the near future but healthcare needs to be digitized for these technologies to penetrate further. The digitization of healthcare procedures will lead to fresh perspectives and hasten the field’s research and innovation. AI-powered Voice recognition can help in this digitization of the healthcare industry while improving efficiency and bettering patient care.


To know more about these solutions please write to us at open-innovator@quotients.com


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Healthtech

Precision oncology, an important development in the fight against cancer

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Precision oncology, an important development in the fight against cancer

The healthcare industry is changing, therefore it’s critical to use new technology to provide new data and help the development of precision medicine (PM). Recent scientific and technical developments have increased our knowledge of illness causation, altered how diseases are diagnosed, and modified how they are treated, making it possible to provide each patient with more precise, powerful, and individualized healthcare. There seems to be a connection between certain illnesses and genetic, genomic, and epigenetic changes.

Precision oncology is an important development in the fight against cancer. It is a cutting-edge kind of cancer therapy that makes sure your care is tailored to a particular type of cancer. It’s the science of tailoring a patient’s treatment plan based on the unique genetics of each patient—the genes that are altered, driving their disease to spread. Precision oncology experts can select a tailored medication for the exact gene alterations that are really causing your cancer after they have identified them. The most recent development in cancer treatment, targeted therapy, offers significantly better outcomes with fewer side effects than conventional chemotherapy. It holds the potential for greater effectiveness, better therapy, and decreased costly and inefficient treatments. The strategies range from the use of targeted medicines to the use of data from next-generation sequencing.

Now with Genomics i.e. the study of all of a person’s genes, new, analytical scientific, and technical advancements have been made, and we may create customized medicines for specific patients or patient populations. The landscape of treatment is shifting as a result of significant advancements in fields like cell and gene therapy, particularly in cancer and uncommon disorders. Precision oncology can also help in the creation of therapies that specifically target a patient’s tumor’s molecular traits. The approach uses tumor specimens for genetic and other molecular studies to enhance cancer diagnosis and care. Precision oncology not only identifies therapy alternatives but also monitors a tumor’s molecular response to an intervention, identifies drug resistance, and studies the mechanisms by which it arises.

In order to fully understand the patient’s germline, tumor exome, and tumor transcriptome, integrative genomics may involve sequencing certain gene panels, exomes, or the complete trio. Despite the fact that sequencing technology’s capabilities are constantly expanding, logistical, regulatory, financial, and ethical issues have prevented the clinic from adopting genomics-driven precision oncology widely. The clinical treatment of cancer patients may be enhanced by integrated clinical sequencing programs used at the point of care. Also, important work remains to prepare the oncological ecosystem to leverage the full potential of personalized medicines.

We have solutions that are advancing innovation and assisting the healthcare industry. Write to us at open-innovator@quotients.com if you’re interested in learning more about these cutting-edge solutions and their expanding use cases.

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