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In an era where technology is reshaping industries, BMO is making waves in the financial sector through its robust artificial intelligence (AI) initiatives and machine learning technologies.

A recent interview with Eric Morrow, Managing Director, Enterprise Data Science & AI, Data & Analytics, and Alex Tait, U.S. Chief Data and Analytics Officer, Data & Analytics, shed light on the transformative power of AI, where increased model performance directly correlates with amplified revenue, reduced costs, and most importantly, enhanced customer experiences.

At BMO, the benefits of broadly applying AI, data science and machine learning are clear. Theres almost endless opportunity for taking data and applying models to it within a financial services organization, said Tait. Potential applications span everything from marketing to defending against cybersecurity threats.

Driving AI integration at BMO

BMO considers AI an integral part of the banks strategy, tightly interwoven with revenue streams and cost-effectiveness. Morrow describes AI integration as a powerful concept that can be applied cross-functionally with active engagement across BMOs lines of business and various Data and Analytics leaders.

On our mobile app, for instance, you provide personalized insights to the individual, and thats a powerful thing that really creates that connection with the customer, he said.

In addition to AIs application in retail banking, BMO has created new and innovative ways to apply advanced analytics to commercial customers needs. Earlier in 2023, Datos Insights, a global advisory firm focused on technology, regulation, strategy, and operations in the financial services industry, presented BMO with a 2023 Impact Innovation Awards in Cash Management and Payments for AI and advanced analytics for its Digital Workbench technology.

BMO Digital Workbench provides real-time analytics and reporting in various areas through a cloud-based self-service portal accessible to multiple businesses within the bank. It integrates scattered data sets across different bank systems with an easy-to-use, cloud-based web interface that drives cohesive and accessible analytics, facilitates insightful customer conversations, and transforms pricing and product mix strategies. A suite of data-driven tools with dynamic customer analytics and forecasting capabilities powers the technology.

Yet implementing customer solutions using AI and analytics is only part of the story. BMO supports cutting-edge research to ensure AI solutions provide functionality, accuracy, efficiency, and automation.

Supporting AIs broader application in financial services

An example is BMOs sponsorship of Next AI, a Montreal-based founder development network for entrepreneurs looking to solve global challenges with AI-based ventures and technology commercialization. Next AI helps identify and support early-stage ventures, which receive access to resources, mentorship education and the network they need to succeed.

Another notable endeavour illustrating BMOs commitment to AI excellence is its partnership with the Vector Institute, a collaboration that epitomizes BMOs dedication to staying at the forefront of AI innovation.

The Vector Institute offers a platform for BMO to explore leading AI research, turning academic insights into practical applications. A past, prominent project involves Natural Language Processing (NLP), a domain critical for a bank dealing with vast amounts of textual data.

According to Morrow, BMO can leverage NLP to help better understand why customers call into contact centres. Being able to ensure that were providing the right level of service and care back to them when there are engagements between the agents is the goal, says Morrow.

And BMO is making progress towards it. In 2023, Digital Banker recognized BMO with an Outstanding Machine Learning Initiative Award, which focused on leveraging NLP association with a contact centre.

The future of AI at BMO

BMOs journey into AI and machine learning isnt just about the present; its about building a future where technology seamlessly integrates with customer needs. The banks strategic direction emphasizes a digital-first approach and cloud-centricity. This focus on technological integration ensures operational efficiency and positions BMO as a pioneer in the banking industrys digital transformation, and it starts from within the bank. This past year, more than 3,500 BMO employees participated in deep technical learning and licensing in subjects like AI, Machine Learning and Cloud.

Additionally, BMO is experimenting with new technologies enterprise-wide to develop digital capabilities to advance the banks Climate Ambition. Through BMOs developing Climate Analytics Platform, the bank is using its digital capabilities, and developing the ability to use AI, to help understand the impacts and risks from weather-related events, such as floods, droughts, extreme heat and more. For any company seeking to manage this evolving risk, it is important to identify how these physical climate impacts are expected to change over time and by location, and how those changes intersect with economic systems. For banks like BMO who have a large financing footprint, it is important to identify where climate hazards are projected to manifest, and to manage the risk and capture opportunities associated with this changing future.While these programs are still under development, our technology teams can support innovation within the bank by piloting new and innovative technology driven approaches to climate related analysis.

BMOs integration of AI is a testament to the banks commitment to delivering value through exceptional banking experiences across all its lines of business. Outside the bank, BMO is raising the bar in applying AI and machine learning in the financial services sector by extending that commitment to research and collaboration with esteemed organizations like the Vector Institute. As AI continues to influence the future, BMO stands as a beacon, showcasing how innovative technology can redefine banking, enrich customer interactions, and drive business growth.

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How AI and machine learning solutions drive value for financial institutions - IT World Canada

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To help detect and remove content that violates their standard policies, LinkedIn has been using its AutoML framework, which trains classifiers and experiments with multiple model architectures in parallel, explain LinkedIn engineers Shubham Agarwal and Rishi Gupta.

We use AutoML to continuously re-train our existing models, decreasing the time required from months to a matter of days, and to reduce the time needed to develop new baseline models. This enables us to take a proactive stance against emerging and adversarial threats.

One of the key points about content moderation is it needs to be enforced and tuned up continuously to address new strategies devised to circumvent it. Additionally, it must adapt to contextual changes. Those include data drift, i.e., inherent changes in content posted on the platform as conversations progress; global events, which tend to surface in discussions and trigger diverse viewpoints, frequently riddled with misinformation; and adversarial threats, which include fraudulent and deceptive practices like creating fake profiles, running scams, and so on.

To address all of those challenges, LinkedIn uses an approach aimed at "proactive detection", which requires a process of continuously adapting and evolving its ML models and systems. AutoML, short for Automated Machine Learning, is a tool LinkedIn created internally to improve machine learning performance by continuously retraining models on new data, correcting them including false negatives and false positives, and fine-tuning their parameters.

Leveraging AutoML, we transformed what used to be a lengthy and intricate process into one which is both streamlined and efficient. [...] After implementing AutoML, we saw the average time required for developing new baseline models and continuously re-training existing ones shrink from two months to less than a week.

Using AutoML, LinkedIn engineers automated the process of data preparation and feature transformation, including noise reduction, dimensionality reduction, and feature engineering, aiming at creating a high-quality training dataset for classifier training.

In a second phase, AutoML experiments with different classifier architectures by searching over a range of hyperparameters and optimization approaches and comparing the performance of the resulting models based on a set of specified evaluation metrics.

Finally, AutoML automates the deployment process by making the newly trained model available to production servers.

According to Agarwal and Gupta, there are still a few areas where their tool needs to mature, specifically to improve speed and efficiency and enable its adoption on a larger scale, which will eventually increase the requirements of computing power. Another promising area, they say, is using generative AI to improve the quality of datasets, both to reduce labeling noise as well as to generate synthetic data for model training.

While not all organizations operate at LinkedIn scale and have the resources to create their own ML automation tools, still the approach described by Agarwal and Gupta may be replicated at a smaller scale to relieve machine learning engineers from the most repetitive tasks associated with retraining existing models.

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How LinkedIn Uses Machine Learning to Address Content-Related Threats and Abuse - InfoQ.com

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Ottawa, Jan. 10, 2024 (GLOBE NEWSWIRE) -- The global Artificial Intelligence (AI) in precision medicine market size is anticipated to reach around USD 8,550 million by 2029, increasing from USD 2,740 million in 2024, a study published by Towards Healthcare a sister firm of Precedence Research.

The imperative for Early Cancer Detection Drives Surge in Precision Medicine Market Fueled by AI advancement, with 609,820 Deaths in 2023 Reported By National Cancer Society, Spotlighting Urgent Need for advanced Solutions

The intersection of artificial intelligence (AI) and precision medicine holds the potential to transform healthcare significantly. Precision medicine techniques aim to identify specific patient phenotypes with uncommon responses to treatment or distinct healthcare requirements. AI plays a pivotal role by employing advanced computational processes and interference, allowing the system to derive valuable Insight, enhance reasoning capacity, and facilitate continuous learning. This, in turn, empowers healthcare practitioners in their decision-making processes through augmented intelligence. Recent literature underscores the importance of translation research in exploring the convergence of AI in Precision medicine, particularly those involving the interplay of non-genomic and genomic determinants. Integrating diverse data sources, including patient symptoms, clinical history and lifestyle, will streamline personalized diagnosis and prognostication.

Download a short version of this report @ https://www.towardshealthcare.com/personalized-scope/5080

Major Principles for Adoption AI in Precision Medication

The incorporation of AI in precise medicine contributes to a notable market increase. AI's enhanced capability optimizes treatment approaches and paves the way for more targeted and personalized healthcare solutions, positively impacting the overall market dynamics.

AI has seen significant growth and acceptance in various domains in the past ten decades, notably within healthcare. AI offers opportunities for intelligent product design, novel services and new business models, yet it also poses social and ethical security, privacy, and human rights challenges. In precise medication, AI technologies range from virtual applications like deep-learning-based health information management systems to cyber-physical implementation, such as robotic assistants in surgeries and targeted nanorobots for drug delivery. Ai's ability to recognize complex patterns has led to image-based detection and diagnostic systems performing better than clinicians. AI-enabled clinical decision support systems can reduce diagnostic errors, enhance decision-making intelligence, and assist in Electronic Health Record (EHR) data extraction.

Precision medicine, notably genotype-guided treatment, has revolutionized healthcare by using genetic information to determine optimal drug dosages, such as warfarin. The Clinical Pharmacogenetics Implementation Consortium provides guidelines for clinicians, enhancing drug therapy through genetic test results. Genomic profiling of tumours aids in tailoring targeted therapies for breast and lung cancer patients. Integrated into healthcare, precision medicine offers precise diagnoses, predicts disease risk preemptively, and designs personalized treatment plans for optimal safety and efficiency. Globally, the trend extends beyond the United States, with initiatives like the UK Biobank, BigBank Japan, and the Australian Genomics Health Alliance showcasing the global impact of changing attitudes towards precision medicine.

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Advancement in Machine Learning Fosters Growth of AI in Precision Medicine Market

The invention of mathematical models that allow AI systems to analyse data, spot pattern and make prediction more accurately and efficiently is a key component of machine learning algorithm advancements. Developments in deep learning architectures, optimization methods, and algorithmic efficiency are frequently included in these improvements. AI is essential to precision medicine because it allows for the customization of medical interventions based on patient characteristics.

The following are some ways that AI advances the field of precision medicine:

In addition, Artificial Intelligence (AI) has the potential to transform the precision medicine industry due to its capacity to analyse a wide range of datasets and its constant refinement of machine learning algorithms. Consequently, this fosters the expansion of artificial intelligence applications that offer more efficient, customized, and focused healthcare solutions.

For Instance,

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Increasing Prevalence of Cancer Leads to a Demand for AI in Precision Medication

In 2023, the American Cancer National Center for Health Statistics collected mortality data and central cancer registries managed incidence data to compile the most recent data on population-based cancer occurrence and outcomes. Society keeps track of new cancer cases and deaths in the United States.

For Instance,

The rapid growth of artificial intelligence (AI) in oncology is fueled by enhanced data capture, increased analytical power, and decreasing cost of genome sequencing. This program has significantly impacted biomedical discovery, diagnosis, prognosis, treatment, and prevention. However, challenges persist in developing inclusive and unbiased AI solutions that are generalised effectively across diverse populations. Concerns include inner biases and the potential for the algorithms to reflect the biases of their creator in the context of cancer care.

Adopting a transparent, thoughtful approach to address bias throughout the entire care is crucial. Integrating AI in Cancer research and precision medicine, leveraging projects like the Cancer Genome Atlas (TCGA), has become pivotal. TCGA, a comprehensive omics data source, provides valuable insights from over 11,000 cancer cases. The convergence of omic data, pathology reports, and medical imaging enables a thorough understanding of cancer's genetic and epigenetic causes, facilitating targeted and preventive measures.

Customize this study as per your requirement @ https://www.towardshealthcare.com/customization/5080

Collaboration and Innovation

In November 2022, Google Collaborate with iCAD they focused on developing innovation and increasing access to mammography technology through cloud-based solutions. The Breast AI Suite from iCAD, a suite of technologies for detecting breast cancer, evaluating density, and determining a person's short-term risk, will soon include Google Health's AI technology. The agreement intends to support the journeys of cancer patients by validating and integrating Google's mammography tools into this portfolio. iCAD plans to integrate Google's mammography AI products into ProFound AI Risk, a clinical decision support platform intended to offer a precise, individualised estimate of the risk of developing breast cancer in the near future. iCAD stated that it intends to enhance the functionality of its algorithm to 2D and 3D mammography by utilizing Google's solutions.

Additionally, the AI driven innovation in cancer detection has contributed to the growth of the AI in precise medicine market, marking a significant advancement in personalised healthcare.

The hurdle of Fairness and Bias in AI for Precision Medicine has Contributed Significantly to Decreased Market Adoption

The inherent biases present in health data, arising from issues such as limited diversity in sampling, missing values, and imputation methods, pose a substantial challenge. When AI models are trained on biased data, they can amplify existing biases, making unfavourable decisions for specific demographic groups based on factors like age, gender, race, geography, or economic status. This bias compromises the clinical applicability of AI models and raises concerns about the overall quality of healthcare outcomes. Patients from underrepresented groups may experience disparities in diagnosis, treatment recommendations, or risk predictions, exacerbating existing inequalities in healthcare. As a result, potential users, including healthcare providers and institutions, may become hesitant to adopt AI solutions due to ethical concerns and the fear of perpetuating or exacerbating biases in patient care. This hesitancy leads to a decrease in the market for AI in precision medicine.

Additionally, addressing fairness and bias in AI models requires concerted efforts, including improving data diversity, implementing bias-mitigation techniques proposed by AI communities, and utilizing tools like IBM's AI Fairness 360 toolkit. Despite these solutions, the nuanced nature of fairness and protected attributes in healthcare necessitates ongoing research and collaboration within the AI and biomedical communities.

Furthermore, weakening stakeholder confidence in these systems and the perceived ethical risks of biased AI models also make stakeholders less willing to invest in and use AI for precision medicine. As a result, it will be difficult for the market for AI in Healthcare to be widely adopted and used.

The Synergy of Technological Advancements and Innovation Propels AIs Role in Precision Medicine Market Growth

Artificial intelligence (AI) in precision medicine, particularly genome-informed prescribing, marks a groundbreaking innovation with significant market opportunities. The power of precision medicine at scale is exemplified in the developing of machine learning algorithms predicting patients' medication needs based on genomic information. Real-time recommendations and personalized dosages rely on genotyping patients in advance. Deep learning techniques, such as those highlighted by Zou and colleagues, contribute to efficient genome interpretation, aiding in identifying genomic variations related to disease presentation, therapeutic success, and prognosis.

In the case of medulloblastoma, AI-mediated analysis has unveiled discrete molecular subgroups, enabling customized treatments for pediatric patients. Precision genomics, steering away from conventional multimodal treatments, allows for targeted chemotherapy, reducing the necessity for radiation. This precision in treatment improves efficacy and minimizes potential neurocognitive sequelae and secondary cancers, presenting a substantial market opportunity.

The integration of AI into imaging recognition has given rise to radio genomics. This novel field links cancer imaging features with gene expression to predict the risk of toxicity after radiotherapy. AI is covering radiogenomic associations in breast, liver, and colorectal cancer. While limited data availability remains a challenge, the growing opportunities in the market for AI in radiogenomics are evident. These findings empower clinicians to select treatments with higher efficacy, presenting additional market opportunities in the evolving landscape of AI-driven precision medicine.

Collaboration and Innovation

Cardiovascular medicine's embrace of predictive modelling, considering factors like gender, genetics, lifestyle, and environment, has led to promising developments. Artificial intelligence, particularly in predictive modelling and combining electronic health records (EHR) with genetic data, offers opportunities to enhance disease prediction and diagnosis. Innovations such as AI-enabled recognition of phenotype features and rapid whole-genome sequencing contribute to faster and more accurate diagnoses, especially in cases of suspected genetic diseases in seriously ill infants.These innovations drive the global increase in the AI market for precise medicine, providing a more nuanced and practical approach to healthcare by integrating diverse data sources and advanced technologies.

Browse More Insights of Towards Healthcare:

Artificial Intelligence in Precision Medicine Market Growth Expanded by Components

In precision medicine, hardware advancements play a crucial role in AI by providing increased computational power for complex data analysis. Specialized hardware accelerators, such as Graphical Processing Units (GPUs) and Tensor Processing Units (TPUs), enable faster processing of vast datasets, facilitating more accurate diagnostics and personalized treatment recommendations.

This fusion of components has fueled the expansion of AI in precise medicine, resulting in improved diagnostic accuracy and personalized treatment approaches, ultimately contributing to the market's overall growth.

Geographical Landscape

North America dominated the AI in the precision medicine market. This region boasts a robust ecosystem of research institutes and healthcare providers collaborating to leverage AI for more accurate Diagnostics, personalized treatment strategies, and improved patient outcomes. Johnson and Johnson, Google, IBM Watson, and NVIDIA.

Europe is anticipated to have the fastest growth in AI in the precision medicine market. Improving early infection diagnosis is becoming increasingly important as the number of older adults rises, and chronic diseases become more common. Many businesses are implementing strategies to give them a competitive edge over rivals. For example, Nuclear and Merck KGaA Darmstadt declared their collaboration to leverage image analysis to find a biomarker platform.

In Asia-Pacific, the multi-country GenomeAsia100K initiative aims to "sequence and analyze the genomes of 100,000 Asian individuals to help accelerate Asian Population-specific medical advances and precision medicine." All discoveries and outcomes will be disseminated to the larger scientific community to distribute the accountability for advancing the sector through R&D among other like-minded establishments and associations.

Competitive Landscape

AI in precise medicine improves diagnosis by analyzing vast datasets, customizing treatment plans based on individual patient characteristics, and speeding up drug discovery through data-driven insight. Leading Companies like IBM, Microsoft, Google, and NVIDIA actively engaged in innovation, contributing to the expansion of AI in the precise medicine market. Notably, AI examined data from thousands of patients in cancer research in partnership with the Cancer Genome Atlas. Genetic anomaliessuch as mutation or overexpressed proteinswere discovered through this collaboration, providing novel therapeutic targets for precision medications. These discoveries make creating highly specialized medicines catered to specific patients possible.

Recent Developments

Market Players

Market Segments

By Technology

By Component

By Therapeutic Application

By Geography

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About Us

Towards Healthcare is a leading global provider of technological solutions, clinical research services, and advanced analytics to the healthcare sector, committed to forming creative connections that result in actionable insights and creative innovations. We are a global strategy consulting firm that assists business leaders in gaining a competitive edge and accelerating growth. We are a provider of technological solutions, clinical research services, and advanced analytics to the healthcare sector, committed to forming creative connections that result in actionable insights and creative innovations.

Web: https://www.towardshealthcare.com

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Artificial Intelligence (AI) in Precision Medicine Market - GlobeNewswire

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TIER IV, a pioneer in open-source autonomous driving (AD) technology, proudly announces the initiation of the Co-MLOps (Cooperative Machine Learning Operations) Project. This new endeavor is aimed at scaling the development of AI (Artificial Intelligence) for autonomous driving. The deployment of the Co-MLOps Platform, developed under this project, will enable the global sharing of managed sensor data, including camera images and LiDAR (Light Detection and Ranging) point clouds, sourced from various regions. Furthermore, the Co-MLOps Platform will offer MLOps functions and Edge AI reference models, empowering partner companies to enhance their proprietary AI for autonomous driving.

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TIER IV is set to exhibit edge AI models atCES 2024inLas Vegas, Nevada, fromJanuary 9-12, following a successful initial proof of concept (PoC) test conducted in 2023 across eight globalregions, includingJapan,Germany,Poland,Taiwan,Turkey, andthe United States. ThePoC test utilized video data collected from these regions to evaluate the perception capabilities of multitask Edge AI models, which have been optimized to operate at less than 10W of power consumption. This showcase at TIER IVs booth will highlight the fruits of this innovative project.

Conventional Challenges

In the development of AI for autonomous driving, large datasets are essential to achieve competitive performance levels. Historically, companies and research institutions have independently collected data and engaged in similar technology development, leading to overlaps in database construction and development processes. Furthermore, limited resources for setting up development environments and data collection have made it challenging for some companies to implement development processes that are robust enough to achieve desired performance levels. This has impacted the scalability of technological development across the industry.

Platform Overview

TIER IV is leading the development of the Co-MLOps Platform, set to serve as a foundation for various companies and research institutions to partake in the development of AI for autonomous driving at an industry-leading level. This initiative aims to catalyze technological development that was previously hindered by insufficient scalability and to foster open innovation through open collaboration. The platform will be structured to fulfill the following objectives:

The development of the platform is being propelled by leveraging the diverse services offered byAmazon Web Services(AWS). AWSs key services such as storage, databases, and computing, as well as its extensive global infrastructure, will serve as the cornerstone for the efficient and stable operations. The platform will also adopt a cloud-native approach, eventually supporting updates to the recognition models via Over the Air (OTA). By leveraging the power of AWS, the Co-MLOps Platform will offer cutting-edge technology and a stable infrastructure, thereby accelerating the development of innovative autonomous driving AI.

The platform fosters differentiation by empowering participating companies to retain their proprietary technologies, functional safety and development processes, and quality control measures. Additionally, integrating the outputs developed on this platform with theOpen AD Kit, defined bythe Autoware Foundationbased on theSOAFEEframework, will expedite software development towards SDV mass production while maximizing the utilization of theArm Automotive platform.

We believe this project will catalyze new collaborations and competitions in AI development within the mobility industry, leading to various innovations, particularly in recognition technologies, saidShinpei Kato, founder, CEO and CTO of TIER IV. Through collaboration with many partners, we aim to develop the worlds leading AI technologies for autonomous driving and promote the rollout of safe and reliable AD technologies.

We anticipate that the construction of a world model will be further boosted by this project, by utilizing a diverse range of large datasets collected worldwide, said ProfessorYutaka Matsuoof theUniversity of TokyosGraduate School of Engineering. The aim is for our joint research to lead to further development of practical applications for autonomous driving through the integration of generative AI technology. We aim to introduce new methods that will enhance the performance of autonomous driving AI.

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We support the vision of this project that aims to solve common challenges in the mobility industry and accelerate the creation of innovation, saidBill Foy, Director of Automotive Solutions and GTM at AWS. By providing comprehensive support using various AWS services and global infrastructure, we will contribute to the long-term success of this project.

Software is changing what it means to own a car today, and to deliver a software-defined vehicle to mass markets requires expertise and collaboration from across the industry, such as through initiatives like SOAFEE, saidRobert Day, SOAFEE SIG Governing Body representative and director of automotive partnerships, Automotive Line of Business, Arm. The Co-MLOps platform project is another important example of leveraging expertise from across the industry to encourage and further accelerate the development and deployment of software-defined vehicles.

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Edge AI Models Set to Be Demonstrated at CES 2024 as a Part of the TIER IV Co-MLOps Project - AiThority

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