Medical generative AI offers immense opportunities to augment clinical processes, including diagnostics, prescriptions and personalised treatments. Chief.AI provides a range of generative AI solutions for the prognostic management and treatment optimisation of hard to treat diseases, including many types of cancer.
Generative AI is a type of machine learning approach that can create novel solutions based on AI models trained on large datasets typically under unsupervised or self-supervised machine learning approaches. This means the AI system has more autonomy in making sense of medical data without significant guidance from human operators, allowing it to process vast quantities of relevant data to achieve more versatile, well-rounded machine intelligence.
When employed to improve medical treatment planning and optimisation, Chief.AI's generative AI service creates highly efficient, informed treatment plans and delivery schedules that can rival or supplant most specialist treatments and interventions.
Chief.AI's Oncomise prognostic management program is trained on extensive datasets encompassing cancer patient histories and disease outcomes.
Such complex, heterogenous datasets may encompass historical patient data including longitudinal health records, demographics, diagnostics and biopsy reports, chemotherapy, radiotherapy, hormonal therapy and surgical histories, and associated patient outcomes. Multimodal training data utilised by foundation models can include images, textual lab results, and genomic sequencing data.
The training of foundation models requires significant computational resources and a high number of GPU hours. Chief AI is able to deploy GPU resources both on premise and in the cloud, dynamically invoking resources where required to optimise self-supervised learning. While generative AI models can be computationally expensive to train, their use cases are diverse and versatile, and as such represent a very good return on investment.
Our medical generative AI models in turn generate sophisticated cancer treatment protocols and treatment sequences, optimising the delivery of chemotherapy drugs, radiotherapy applications, immunotherapy and surgical interventions to improve oncology outcomes while significantly improving the patient's quality of life during and after treatment.
Generative AI algorithms can learn patterns, form cognitive structures, and derive meaning out of vast amounts of unstructured, clinically relevant historical data that has accumulated in hospitals, diagnostics and pathology departments, and other clinical settings.
Generative AI models typically require self-supervised training on unlabelled data, and are therefore more readily trainable on local data repositories on hospital premises. This may yield training efficiencies where data preparation and labelling requirements are minimised.
High quality unstructured medical data when exposed to specialist algorithms will then result in the emergence of healthcare domain-focused foundation models. This implies that the intrinsic characteristics of high quality healthcare data in conjunction with particular types of training algorithms will allow a model to emerge, creating AI that is more objective and grounded in the features of the underlying data.
Once trained, the foundation model essentially becomes smart enough to generate treatment optimisation solutions and highly customised care workflows to assist a prescribing oncologist or medical consultant. Two heads are better than one, goes the old adage; a medical board may be better than a single doctor. However, in this case, large high quality training datasets ensure that the resultant generative AI is the equivalent of the medical opinions of hundreds or even thousands of medical professionals.
Let's take a look at AI deployment for oncology as one use case. Generated oncology protocols will specify how much and how long of each treatment to use in a clinical setting, essentially giving rise to generative AI-powered personalised medicine. After incorporating features such as disease stage, severity, comorbidities and potential contraindications, the AI models are fitted to real-world clinical test data to validate, with several steps until model drift is eliminated and the software is satisfactorily fitted to the medical use case at hand.
When the generative medical AI model is deployed, chemotherapy regimens can thus be combined with radiotherapy, immunotherapy and hormonal therapy sequences in a personalised treatment plan that is unique to each patient. Generative AI thus makes individual prognostic management more efficient and well informed.
Though the impact of generative AI on personalied oncology is important, it is by no means the only area in which generative AI can revolutionise medicine. A myriad of other medical use cases, including cardiology, intensive care, and surgical interventions - both emergency and elective - stand to reap significant benefits from generative AI-powered decision-making and treatment planning.
Consider, for instance, the field of cardiology. Every patient's heart condition is unique, influenced by a complex mix of genetics, lifestyle, and other health factors. Generative AI models have the potential to learn from the vast pool of patient data, including the variations in these factors, and use this knowledge to aid medical professionals in making more informed decisions. It could offer valuable insights on whether to perform a coronary angioplasty, or when to start or stop administering beta-blockers or anti-coagulants.
Moreover, predictive models will be developed to forecast the likelihood of future cardiac events based on a patient's specific profile, thereby enabling preemptive treatment plans. By doing so, generative AI not only has the potential to improve patient outcomes but would likely help to significantly reduce healthcare costs through prevention and early intervention.
Similarly, in the context of intensive care units (ICUs), generative AI could revolutionise care by predicting patient deterioration, aiding in the diagnosis of complex conditions, and recommending optimal treatment strategies. Given the high-stakes nature of ICUs, where timely and accurate decision-making can dramatically affect patient outcomes, the application of generative AI could prove invaluable.
AI is used to identify patterns that might indicate a decline in patient health before it becomes noticeable to clinicians. This would trigger early interventions that could prevent further deterioration. Additionally, AI will eventually assist in managing the complex and diverse conditions seen in ICU patients, many of which can be challenging to diagnose and treat. Generative AI could provide doctors with a highly effective tool to support their decision-making in these challenging environments, supplementing their expertise with the ability to process and interpret vast quantities of data in real time.
For elective and emergency surgeries, generative AI would aid in risk assessment, preoperative plan generation, and postoperative care. Preoperatively, AI could estimate surgical risks by considering a wide array of factors, including patient health status, type and complexity of the surgery, and surgeon's experience. These predictions could help in making informed decisions about whether to proceed with surgery and in setting realistic expectations for the patient.
Intraoperatively, generative AI could assist in surgical planning by generating patient-specific models and simulations, enabling surgeons to plan and practice complex procedures beforehand. Postoperatively, AI could monitor patient recovery and predict complications, enabling timely interventions and reducing hospital stay duration.
As Chief.AI moves forward with building these transformative AI models, it's crucial to underscore the importance of ethical considerations and adherence to healthcare governance standards. As these models are being trained on extensive datasets of patient information, privacy and consent are paramount. Chief.AI is therefore ensuring their models conform to the latest healthcare governance standards.
Furthermore, an essential aspect of the successful integration of AI in healthcare is the interpretability of the AI models. Clinicians must be able to understand and trust the decisions made by the AI. Therefore, Chief.AI is focusing on building models that provide clear and understandable reasoning for their decisions, to help clinicians feel confident in integrating these AI tools into their practice.
Chief AI’s generative AI services can consume vast amounts of healthcare-focused information and patient data, yielding personalised medical treatments for a range of conditions. Yet, this practice raises the concern of ensuring the privacy of both the patient as well as the healthcare provider during computation and execution. Confidential AI emerges as a compelling solution, isolating data during execution and thus ensuring a robust shield against potential cyber attacks and internal threats - threats that may be malicious or even behavioural.
Confidential computing solutions developed under our larger machine learning frameworks create hardware and software level restraints on generative AI data connectivity, adhering strictly to enterprise policies and code, and containing outputs within a trusted and secure infrastructure. Data silos, while adequately exploited for machine learning, are managed internally with respect to role and departmental level privileges. With confidential computing, the integrity of data utilised for Chief AI's healthcare models is assured. Furthermore, it ensures that the AI models only learn from data intended for use, thereby reducing inadvertent compliance risks.
As we can see, generative AI's potential to revolutionise personalised medicine is just beginning to be tapped. As we continue to explore and refine these models over both public cloud-based infrastructure, as well as local deployments tailored for confidential healthcare settings, we can look forward to a future where every patient receives truly personalised care, driven by the power of AI.