News and updates from the world of AI and ML
The introduction of the Fractional Chief AI Officer at Chief.AI marks a significant milestone for businesses aiming to harness the full potential of their data and AI capabilities.
Transforming Data into a Valuable Asset
Often, valuable data lies dormant within the confines of different business departments, akin to a smartphone without a network connection. The Fractional Chief AI Officer service brings a transformative approach to this challenge. It’s about taking this dormant data and turning it into a treasure trove of insights, ready for machine learning applications and primed to reveal hidden gems that can propel a business forward.
Redefining Data Governance
In the rapidly evolving world of AI, staying on top of regulatory compliance is more crucial than ever. The dedicated Chief AI Officer service steps in as a much-needed navigator in this complex terrain, ensuring that businesses not only comply with the latest regulations but also maintain robust data privacy and security practices.
Harnessing AI Beyond Basics
This service isn’t just about adopting AI technologies; it’s about embedding them into the core of your business strategy. Think of it as having an in-house AI expert who helps you make the most of tools like ChatGPT, optimizing their usage across departments, keeping an eye on costs, and ensuring that your AI investments are truly paying off.
A Strategic Ally in Your AI Endeavors
Imagine having a dedicated AI strategist, a fractional Chief AI Officer, who aligns your AI initiatives with your broader business goals. This role isn’t just about overseeing AI deployment; it’s about integrating AI into your business DNA, ensuring it contributes meaningfully to your overall objectives.
Commitment to Ethical AI
In an era where AI ethics are under the microscope, partnering with Chief AI means choosing a path of responsibility and clarity. It’s about making AI decisions that are fair, transparent, and accountable. This service helps demystify AI, making its workings understandable and trustable within your organization.
Elevating Data Governance to New Heights
Data governance with Chief AI goes beyond mere compliance; it’s about unleashing the full power of AI while upholding the highest standards of data privacy and security.
Simplifying the Complex World of AI Compliance
Navigating AI regulations can feel like finding your way through a fog. The Chief AI service acts as your compass, guiding you towards comprehensive compliance in an ever-changing regulatory landscape.
Mitigating AI Risks with Expertise
Embarking on an AI journey comes with its set of challenges and risks. This is where Chief AI steps in, offering expert guidance to identify and mitigate potential pitfalls, from data privacy concerns to optimizing system performance.
Final Thoughts
The launch of the Fractional Chief AI Officer service is more than just an announcement; it’s a shift in how businesses approach and integrate data and AI into their strategies. It’s an invitation to embark on a journey where AI is not just a tool but a pivotal part of your business’s growth and success. Ready to explore this new territory? Chief AI is here to lead the way.
Breast cancer has historically been a significant health challenge for women worldwide. Over the past few decades, we’ve witnessed considerable progress in the way breast cancer is diagnosed and treated, with substantial improvements in patient survival and quality of life. Now, as we look toward the future, Generative Artificial Intelligence is poised to revolutionise breast cancer care.
In the early 1990s, the primary methods for detecting breast cancer were manual breast examinations and mammography. While these methods were useful, they had their limitations, and there was a significant risk of late detection or misdiagnosis.
Treatment options at the time were predominantly invasive and generalized. Lumpectomy or mastectomy were standard procedures, often followed by radiation or systemic chemotherapy that affected the entire body, leading to various side effects. Hormone therapy was available but not extensively personalized, and targeted therapies were in their infancy.
The 2000s marked an era of advancements with the emergence of digital mammography, providing clearer images and better detection rates. The advent of molecular diagnostic tools, like Oncotype DX, also allowed for a more tailored approach to therapy, particularly in deciding which patients would benefit from chemotherapy.
The last decade has seen further improvements with the development of 3D mammography and automated whole breast ultrasound, enhancing early detection, especially in women with dense breasts. Treatments have also become more personalized, with monoclonal antibodies, like Trastuzumab, for HER2-positive breast cancer, and CDK4/6 inhibitors for hormone receptor-positive disease. Despite these advances, challenges persist, especially in terms of predicting patient-specific responses and minimizing treatment side effects. This is where our AI for precision oncology comes in.
Generative AI, a revolutionary subset of machine learning deployed for oncology by Chief AI, is capable of creating new, innovative solutions by creating foundation models that are trained on very large, multi-faceted datasets. Generative AI’s real transformative potential is its ability to autonomously make sense of vast amounts of breast cancer-specific data, thereby enabling a new level of machine intelligence through foundation models.
As a leader in this cutting-edge field, Chief.AI’s Generative AI model, Oncomise, offers an unprecedented opportunity to revolutionize breast cancer care. Oncomise trains on extensive datasets that encompass a multitude of patient histories, disease outcomes, treatment responses, and more. This includes detailed data, such as mammography images, biopsy results, surgical histories, genomic sequencing data, and longitudinal health records.
The utility of Generative AI for precision medicine becomes evident when it is deployed in a real-world clinical setting. The model generates intricate treatment protocols, integrating chemotherapy, radiation, hormonal therapies, and immunotherapy based on each patient’s unique disease profile. This truly epitomizes the concept of precision medicine – delivering the right treatment to the right patient at the right time.
By tailoring treatment plans to individual patient profiles, accounting for factors like cancer stage, genetic mutations, comorbidities, and potential contraindications, Generative AI helps to reduce unnecessary treatments, minimize side effects, and enhance overall treatment outcomes.
Furthermore, Generative AI has immense potential in predictive oncology – the ability to anticipate disease progression and treatment response. This aspect can dramatically improve prognosis, help in timely intervention, and potentially increase survival rates in breast cancer patients.
As we look ahead, it is clear that Generative AI is not merely an incremental improvement in breast cancer care but a paradigm shift in precision oncology. As it evolves, it promises to enhance every aspect of breast cancer management, from diagnosis to prognosis, treatment planning, and monitoring disease progression. With continued investment and research in this space, we can look forward to a future where every breast cancer patient receives personalised care, improving outcomes and, ultimately, survival rates. In doing so, Generative AI will enable us to continue our journey towards conquering breast cancer.
Artificial Intelligence, with its multifarious subsets, presents extraordinary opportunities to augment clinical processes, such as diagnostics, prescriptions, and personalised therapeutic strategies. One of these areas, generative AI, has caught the spotlight for its potential in revolutionising healthcare, particularly in managing complex diseases like cancer.
As an early pioneer in this burgeoning field, Chief.AI is leveraging generative AI to pioneer transformative solutions in the prognostic management and optimisation of treatment strategies for notoriously difficult-to-treat diseases, including various types of cancer.
Deciphering Generative AI and Its Utility in Healthcare
Generative AI, a variant of machine learning, has the capacity to formulate new solutions based on AI models educated on voluminous datasets. A majority of these models are created utilising unsupervised or self-supervised machine learning approaches. This results in an AI system with increased autonomy, capable of processing massive amounts of medical data, thereby amplifying the machine’s intellectual capacity.
For instance, Chief.AI’s flagship oncology management system, Oncomise, utilises vast datasets to form a comprehensive understanding of patient histories and disease outcomes across various cancer types, including prostate and lung cancer.
Such datasets are a tapestry of diverse patient data including longitudinal health records, demographics, diagnostic and biopsy reports, therapeutic histories encompassing chemotherapy, radiotherapy, hormonal therapy, and surgical interventions, coupled with related patient outcomes. The training data, which includes images, textual lab results, and genomic sequencing data, provides the foundation models a rich context to learn from.
Although the training of these foundation models demands considerable computational resources and extensive GPU hours, the diverse application and transformative potential of these generative AI models guarantee a significant return on investment.
Conception of Enhanced Cancer Treatment Strategies
By capitalising on patterns discerned from extensive unstructured clinical data, generative AI models are capable of fashioning intricate cancer treatment protocols. These sophisticated solutions optimise the delivery of various treatments – from chemotherapy drugs to surgical interventions – thereby aiming to enhance oncology outcomes. Concurrently, the personalised protocols improve the patient’s quality of life during and post-treatment, a crucial element often overlooked in traditional treatment plans.
The Emergence of AI-Curated Medical Treatment Protocols
The beauty of generative AI resides in its capacity to derive insight from vast amounts of unstructured, clinically pertinent historical data housed in hospitals and other clinical settings. The training of AI models on such high-quality medical data culminates in the emergence of specialised, healthcare-focused foundation models. The result is an AI that is not just more objective, but deeply rooted in the data it’s trained on.
Once suitably trained, these foundation models can generate highly customised care workflows and treatment optimisation solutions, thereby aiding oncologists and medical consultants in their decision-making process.
The Advent of AI-Driven Personalised Medicine in Oncology
When one considers oncology, the AI’s capabilities extend to generating specific protocols that determine the duration and dosage of each treatment, thereby heralding a new era of personalised medicine facilitated by AI. These models consider various factors like disease stage, severity, comorbidities, and potential contraindications, and then are validated against real-world clinical data. The iterative refining process continues until an adequate fit is achieved for the specific medical use case.
Upon deployment, generative AI can create personalised treatment plans for both common and rare cancers. In the case of prostate cancer, it can suggest a combination of surgery, radiation therapy, and hormone therapy, tailored to the patient’s specific diagnosis and health status. Similarly, for lung cancer, the AI can provide a personalised plan integrating surgery, radiation, chemotherapy, targeted therapy, or immunotherapy, each modality in specific sequences and dosages.
Broadening the Impact of Generative AI
While its utility in optimising cancer treatment protocols is indeed profound, the reach of generative AI in healthcare extends beyond the realm of oncology. Cardiology, intensive care units, and surgical disciplines – both emergency and elective – are other areas that stand to gain significantly from generative AI-driven decision-making and treatment planning.
For instance, in cardiology, decisions such as whether to perform coronary angioplasty or adjust medication schedules could be substantially improved by the insights provided by AI.
To that end, Chief.AI is creating generative AI models that conform to the latest healthcare governance standards, thereby ensuring the seamless integration of this pioneering technology into both private and public sector healthcare practices. This is the dawn of a new era, and Chief.AI is leading the charge in making generative AI a cornerstone of modern healthcare.
Chief AI creates a new range of foundation models for healthcare applications. In this article, we will examine what it means for generative AI to emerge from unstructured data in healthcare settings.
In the context of foundation models in healthcare, the term “emergence” refers to the unplanned development of complex patterns, behaviors, or capabilities in a system, stemming from the interaction of simple elements or rules. These emergent properties are not directly programmed into the model but instead arise as a natural consequence of the training process and the underlying healthcare-related data.
For example, a foundation model in healthcare may be a large-scale machine learning model trained on diverse medical data, such as electronic health records, medical literature, and patient interactions. During training, it learns to generate insightful predictions or recommendations by identifying patterns and relationships within the data. As the model becomes more sophisticated, it starts to exhibit complex behaviors, such as understanding medical context, making diagnostic suggestions, and even generating treatment plans. These capabilities are emergent properties because they are not explicitly programmed into the model but rather develop from its learning process and the vast amount of healthcare data it is exposed to. This leads to the creation of generative AI in medicine.
Now let’s take a look at the implications for personalised medicine and individualised treatments. Foundation models in healthcare can play a significant role in personalised medicine, an approach that tailors medical treatment to each individual patient based on their unique genetic, environmental, and lifestyle factors. By leveraging the power of emergence, these models can analyse vast amounts of data to identify subtle patterns and relationships, enabling more accurate predictions and personalised treatment plans.
As foundation models are exposed to diverse data sources, including genomic data, they can develop an emergent understanding of the intricate relationships between genetic variations, disease risk, and treatment responses. By integrating this knowledge with other relevant data such as electronic health records, medical literature, and real-world evidence, foundation models can provide healthcare professionals with insights into the most effective treatment options for a specific patient. This personalised approach can lead to improved patient outcomes, reduced side effects, and increased cost-effectiveness in healthcare.
Moreover, the power of emergence in foundation models also extends to the development of new drugs and therapies. By analysing data from drug discovery research, clinical trials, and patient populations, these models can identify potential therapeutic targets and predict drug efficacy in specific patient groups. This information can guide researchers in designing tailored therapies, leading to more effective and safer treatments for patients with unique genetic profiles or specific disease subtypes.
In the context of medical generative AI, therefore, emergence in foundation models in healthcare refers to the development of complex behaviours and capabilities that arise from the interaction of simpler elements or rules during the training process, without explicit programming. This can lead to novel insights and improved decision-making in various healthcare applications, such as diagnostics, treatment planning, and patient care.
This is a step by step guide for radiologists (veteran and aspiring) to help build a plan for utilizing AI to improve efficiency in radiology operations.
There are several key areas in which radiology workload on CT scan images versus MRI images differs:
In summary, CT scans are used for detailed, high-contrast images of bones and other hard tissues, while MRI is used for better visualization of soft tissue structures. CT scans are faster to complete and require the use of contrast agents, while MRI is a longer process and doesn’t require them. Radiologists need to adapt their workflow to the different peculiarities of each modality, and also need to be familiar with the indications of use, and the strengths and limitations of each modality. AI can be adapted to accelerate diagnoses on both imaging modalities.
By following these steps, radiologists can use AI to diagnose patients more efficiently, reducing the time it takes to make a diagnosis and potentially leading to faster treatment and better outcomes for patients.
The Chief.AI Marketplace is a direct-to-consumer marketplace for buying, selling and running machine learning algorithms.
We offer a wide range of algorithms that are useful across various domains – for example, you can use a ‘compound image’ classifier that detects compound images (images made up of multiple graphs/images, as are often found in scientific publications). Another algorithm from our partners at MDC classifies scientific research figures into various sub types such as flow cytometry, mass spectrometry, Western Blots and more. Learn more and try them out on the marketplace!
The web interface is pretty intuitive, you just click on models and can upload test data directly to the platform for inference. You get 10,000 credits upon signing up and it costs roughly 10 credits per execution – but drop us a line and we can top you up for free as an early adoptor if you manage to use them up! We also have RESTful API’s you can use to integrate models into your own apps and workflows; this will be coming online in the next few weeks.
You might notice a lot of models are from the Medicines Discovery Catapult – a cutting edge research organisation backed by the UK government to develop & commercialise the next generation of medicines.
Their multi-disciplinary team of scientists are open-sourcing tools they’ve found useful for their own research, which focuses quite a bit on analytical chemistry and biology. We think these tools will be of great use to the wider research community!
We’ll be on-boarding more models and suppliers as part of our soft launch programme; if you feel you’d like to contribute machine learning or other algorithms to the platform, please get in touch!
Both open-source, commercial and FDA-approved algorithms are welcome. We’re also working to on-board FDA approved medical algorithms as part of a drive to bring ‘medical grade’ algorithms to the wider research and scientific community. Keep your eyes peeled for updates!
Artificial intelligence is a great way to get value out of business data. However, as with all business tools, there are costs involved. These costs should ideally be optimized in order to ensure your business gets the most out of its AI investment.
In the conventional machine learning setup, there are costs associated with the purchase of infrastructure; GPUs, CPUs, network and orchestration infrastructure such as bare metal servers and load balancers.
Then there is the human resources cost to consider. Machine learning researchers are required to not just train models, but also deploy and monitor them; then compare and analyse the results from one execution to another.
As you can imagine, these costs can add up quickly.
That’s where a Pay As You Go machine learning orchestrator such as Chief.AI can really make life simple for data scientists. Built on scalable, elastic compute infrastructure, an AI orchestrator can marshal, power up, run, document and destroy AI models on the fly, resulting in measured operations and significant cost savings over the long run.
If your entire AI operation is pay as you go, you can have control over your AI costs, providing more value to your AI investment over the long run.