A quick survey of academic and commercial developments in the industry sector – healthcare- that remains at the tip of the spear in the hybrid development of applied technologies in AI and Digital Twins, including:
- Digital twin in healthcare: Recent updates and challenges
- Brains of the Operation: Atlas Meditech Maps Future of Surgery With AI, Digital Twins
- Generative artificial intelligence empowers digital twins in drug discovery and clinical trials
- BigBear.ai Expands Partnership with Thomas Jefferson University Hospital
- What Next?: The Future of AI-driven Healthcare and Digital Twins
Featured Image Source: NVIDIA
Digital twin in healthcare: Recent updates and challenges
As with all innovation (arguably since the framing of the open source software movement with the release of the Linux OS), a consistent, constant force function in the exponential acceleration of research and development of technology is the crowdsourcing of research through the peer review process globally. In the end, this global sharing of scientific and technical information was the core design principal behind ARPANET and it continues to transform the world.
This paper is not only interest for its findings, but is authored by three Chinese researchers – offering a window into any competitive advantage the Chinese may have in this space and any cultural nuisances of the research and development process (or, what is more the case with this paper, that global science offers a common language and framework for how to communicated with fellow practictioners on a global basis).
About the Authors
Tianze Sun: Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian, People’s Republic of China; Key Laboratory of Molecular Mechanism for Repair and Remodeling of Orthopedic Diseases, Dalian, People’s Republic of China
Xiwang He: School of Mechanical Engineering, Dalian University of Technology, Dalian, People’s Republic of China
Zhonghai Li: Zhonghai Li, Department of Orthopedics, First Affiliated Hospital of Dalian Medical University, Dalian 116000, People’s Republic of China
Abstract
As simulation is playing an increasingly important role in medicine, providing the individual patient with a customised diagnosis and treatment is envisaged as part of future precision medicine. Such customisation will become possible through the emergence of digital twin (DT) technology. The objective of this article is to review the progress of prominent research on DT technology in medicine and discuss the potential applications and future opportunities as well as several challenges remaining in digital healthcare. A review of the literature was conducted using PubMed, Web of Science, Google Scholar, Scopus and related bibliographic resources, in which the following terms and their derivatives were considered during the search: DT, medicine and digital health virtual healthcare. Finally, analyses of the literature yielded 465 pertinent articles, of which we selected 22 for detailed review. We summarised the application examples of DT in medicine and analysed the applications in many fields of medicine. It revealed encouraging results that DT is being increasing applied in medicine. Results from this literature review indicated that DT healthcare, as a key fusion approach of future medicine, will bring the advantages of precision diagnose and personalised treatment into reality.
Strengths
This review was conducted to introduce the DT technology and summarise its applications in medicine. It will help in identifying the strengths and limitations of DT in medicine. It also showed the potential and opportunities of this technology to further application in all the medical fields. Future studies could include these aspects to expand this literature review or make more attempts of DT in medicine based on the existing researches.
Limitations
The search in this review was restricted to English articles only, which might result in the omission of some publications. Also, we restricted the search to articles that included the terms digital twin, healthcare or respective Medical Subject Heading terms. Some relevant papers which did not explicitly use these terms but dealt with relevant topics might be ignored. In addition, the application of DT in medicine is currently in the preliminary stage, and most of them are application attempts. So there is a lack of randomised controlled trials for quality assessment.
Conclusion
In this review, we studied the DT technology and the applications of DT in medicine. We concluded that DT will be more widely used in the medical field to solve the problems, such as real-time monitoring, dynamic analysis and precise treatment for diseases, which cannot be fully explained by traditional methods. It can model the perception and action of any relevant facility in the medical environment, coupling the observable state of the DT with the state of the physical entity (PE). Although, DT technology has technical and ethical problems in the medical field that need to be solved urgently, the progress is encouraging. The use of DT in medicine is not only limited to the diagnosis and treatment of diseases, but can also be used for the prediction of health and disease states, which provide a quantitative understanding of health and disease. DT healthcare, as a key fusion approach of future medicine, will realise precise medicine and bring the advantages of personalised treatment to reality.
For the full paper, go to Digital twin in healthcare: Recent updates and challenges.
Brains of the Operation: Atlas Meditech Maps Future of Surgery With AI, Digital Twins
Just as athletes train for a game or actors rehearse for a performance, surgeons prepare ahead of an operation. Now, Atlas Meditech is letting brain surgeons experience a new level of realism in their pre-surgery preparation with AI and physically accurate simulations. Atlas Meditech, a brain-surgery intelligence platform, is adopting tools — including the MONAI medical imaging framework and NVIDIA Omniverse 3D development platform — to build AI-powered decision support and high-fidelity surgery rehearsal platforms. Its mission: improving surgical outcomes and patient safety.
Custom 3D Models of Human Brains
A key benefit of Atlas Meditech’s advanced simulations — either onscreen or in immersive virtual reality — is the ability to customize the simulations, so that surgeons can practice on a virtual brain that matches the patient’s brain in size, shape and lesion position.
Realistic Rehearsal Environments for Practicing Surgeons
Atlas Meditech is using NVIDIA Omniverse to develop a virtual operating room that can immerse surgeons into a realistic environment to rehearse upcoming procedures. In the simulation, surgeons can modify how the patient and equipment are positioned. Using a VR headset, surgeons will be able to work within this virtual environment, going step by step through the procedure and receiving feedback on how closely they are adhering to the target pathway to reach the tumor. AI algorithms can be used to predict how brain tissue would shift as a surgeon uses medical instruments during the operation, and apply that estimated shift to the simulated brain.
Dr. Cohen-Gadol believes that in the coming years AI models will be able to further enhance surgery by providing additional insights during a procedure. Examples include warning surgeons about critical brain structures that are adjacent to the area they’re working in, tracking medical instruments during surgery, and providing a guide to next steps in the surgery.
Generative artificial intelligence empowers digital twins in drug discovery and clinical trials
About the Authors
Maria Bordukovaa and Nikita Makarov: Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Munich (RICM), Penzberg, Germany; Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Munich, Germany; Department of Biology, Ludwig-Maximilians University Munich, Munich, Germany
Raul Rodriguez-Esteban: Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Basel (RICB), Basel, Switzerland
Fabian Schmich: Data & Analytics, Pharmaceutical Research and Early Development, Roche Innovation Center Munich (RICM), Penzberg, Germany
Michael P. Menden: Institute of Computational Biology, Computational Health Center, Helmholtz Munich, Munich, Germany;c Department of Biology, Ludwig-Maximilians University Munich, Munich, Germany; Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, Australia; German Center for Diabetes Research (DZD e.V.), Munich, Germany
Introduction
The concept of Digital Twins (DTs) translated to drug development and clinical trials describes virtual representations of systems of various complexities, ranging from individual cells to entire humans, and enables in silico simulations and experiments. DTs increase the efficiency of drug discovery and development by digitalizing processes associated with high economic, ethical, or social burden. The impact is multifaceted: DT models sharpen disease understanding, support biomarker discovery and accelerate drug development, thus advancing precision medicine. One way to realize DTs is by generative artificial intelligence (AI), a cutting-edge technology that enables the creation of novel, realistic and complex data with desired properties.
Areas covered
The authors provide a brief introduction to generative AI and describe how it facilitates the modeling of DTs. In addition, they compare existing implementations of generative AI for DTs in drug discovery and clinical trials. Finally, they discuss technical and regulatory challenges that should be addressed before DTs can transform drug discovery and clinical trials.
Expert opinion
The current state of DTs in drug discovery and clinical trials does not exploit the entire power of generative AI yet and is limited to simulation of a small number of characteristics. Nonetheless, generative AI has the potential to transform the field by leveraging recent developments in deep learning and customizing models for the needs of scientists, physicians and patients.
Article highlights
- Digital Twins are emerging at all stages of drug discovery and development.
- Generative artificial intelligence (AI) is poised to be the underlying technology supporting future development of Digital Twins.
- A number of both academic and commercial Digital Twins can be found in preclinical drug development models.
- Digital Twins in clinical trials still need further development, both methodological and from a regulatory standpoint.
- It is anticipated that there are many use cases where Digital Twins will be applied in clinical trials in the near future, including interim trial analysis, adverse event prediction and in trial design.
- The development of a foundation model may further advance Digital Twins in drug discovery and clinical trials.
For the full paper, go to Generative artificial intelligence empowers digital twins in drug discovery and clinical trials.
BigBear.ai Expands Partnership with Thomas Jefferson University Hospital
Bigbear.ai usually surfaces in our research in a national security investment context (after the company, in early 2023, was awarded a $900M, decade-long (indefinite delivery/indefinite quantity agreement) US Air Force contract.
It is fascinating to see this BigBeaer.ai use case in the heathcare operations space:
BigBear.ai a leading provider of AI-powered business intelligence solutions announced that Thomas Jefferson University Hospital is expanding its use of BigBear.ai’s AI platform to make day-to-day operations improvements to centralized resource and bed management, nurse staffing, and its transfer center. BigBear.ai’s FutureFlow RX® Predictive AI platform allows hospitals to create ‘digital twins.’ Hospitals can then apply likely future operational scenarios to their digital twins, seeing how the digital twin hospital responds to myriad stresses. This predictive knowledge empowers hospitals to plan and execute more efficiently and effectively. During a long-term strategic planning exercise with Thomas Jefferson University Hospital, FutureFlow RX tested likely upstream and downstream outcomes based on predicted hospital volume growth and demand shifts. As a result, Jefferson was then able to optimize its master facility plan to meet future patient needs best.
What Next?
The Future of AI-driven Healthcare and Digital Twins
The future of AI-driven healthcare and digital twins holds significant promise for revolutionizing how we approach medical diagnosis, treatment, and patient care. Here are some key aspects of their potential future development:
- Personalized Medicine: AI can analyze vast amounts of patient data, including genetic information, lifestyle factors, and medical history, to create personalized treatment plans. Digital twins, which are virtual representations of individual patients, can be used to simulate and optimize treatment strategies, taking into account the unique characteristics of each person.
- Diagnostics and Early Detection: AI algorithms can assist in the early detection of diseases by analyzing medical images, such as X-rays, MRIs, and CT scans. Digital twins can simulate disease progression and help identify potential issues before they manifest in the physical body, enabling proactive interventions.
- Drug Discovery and Development: AI-driven approaches can expedite drug discovery by analyzing vast datasets to identify potential drug candidates and predict their efficacy. Digital twins of organs or biological systems can be used to simulate the effects of drugs, allowing researchers to optimize dosages and anticipate potential side effects.
- Remote Patient Monitoring: Digital twins can continuously monitor a patient’s health in real-time, providing valuable insights to healthcare providers. AI algorithms can analyze this data to detect anomalies or changes in health conditions, enabling timely interventions and reducing the need for frequent hospital visits.
- Robotics and Surgery: AI-powered robotics can enhance precision in surgical procedures. Digital twins can be used to simulate surgeries beforehand, allowing surgeons to plan and practice complex procedures. During surgery, AI can assist in real-time decision-making and improve overall accuracy.
- Healthcare Workflow Optimization: AI can streamline administrative processes, automate routine tasks, and improve the overall efficiency of healthcare systems. This can free up healthcare professionals to focus more on patient care and complex decision-making.
- Patient Engagement and Education: Digital twins can serve as educational tools for patients, helping them better understand their conditions and treatment options. AI can provide personalized health information, reminders for medications, and lifestyle recommendations to encourage healthier choices.
- Ethical and Regulatory Challenges: As AI becomes more integrated into healthcare, addressing ethical concerns related to data privacy, security, and the responsible use of AI will be crucial. Regulatory frameworks will need to evolve to ensure the safe and ethical deployment of these technologies.
It’s important to note that while the potential is vast, challenges remain, including the need for robust data governance, addressing biases in AI algorithms, and ensuring the technology is accessible and affordable for all. The future of AI-driven healthcare and digital twins will likely involve a combination of technological advancements, regulatory frameworks, and ongoing collaboration between healthcare professionals, researchers, and technology developers.
Additional OODA Loop Resources
Technology Convergence and Market Disruption: Rapid advancements in technology are changing market dynamics and user expectations. See: Disruptive and Exponential Technologies.
The New Tech Trinity: Artificial Intelligence, BioTech, Quantum Tech: Will make monumental shifts in the world. This new Tech Trinity will redefine our economy, both threaten and fortify our national security, and revolutionize our intelligence community. None of us are ready for this. This convergence requires a deepened commitment to foresight and preparation and planning on a level that is not occurring anywhere. The New Tech Trinity.
Benefits of Automation and New Technology: Automation, AI, robotics, and Robotic Process Automation are improving business efficiency. New sensors, especially quantum ones, are revolutionizing sectors like healthcare and national security. Advanced WiFi, cellular, and space-based communication technologies are enhancing distributed work capabilities. See: Advanced Automation and New Technologies
Rise of the Metaverse: The Metaverse, an immersive digital universe, is expected to reshape internet interactions, education, social networking, and entertainment. See Future of the Metaverse.
