Spotlight #15: 🔮 Google's predictions, 👵 Digital ageism, 💺 World Economic Forum recap, 💊 MIT's Antibiotics-AI Project
AI Health Hub, 22/01/2024
🔮 3 predictions for AI in healthcare in 2024
Blog
The first newsletter post of the year had to include a blog post on some predictions for 2024. This one is brought to us by Google Health. Nothing too far from what we have been seeing and including in our newsletter, but it was still worth reading as Google has been a major player in the past months.
The blog post starts with a recap of the latest gen AI development within Google (MedLM and Vertex). The first prediction is about the continued effort to optimise the administrative work of caregivers, with the goal of easing the burden and enhance satisfaction. This technology is also preparing the sector for broader transformations, as it can change clinical documentation, doctor-patient interactions and ultimately bring better outcomes for patients. Examples in that directions are how gen AI being used to improve patient handoffs between nurses or to summarise electronic health records. Finally, 2024 will be about deepening our understanding of the best ways to use gen AI in healthcare, including analytic rigor, empathy and economic necessity.
👵 The AI cycle of health inequity and digital ageism: mitigating biases through the EU regulatory framework on medical devices
Paper
Discrimination can happen based on several grounds, and one of them can be age, which sooner or later concerns us all. This paper dives into the risks of age-related biases in new technology, referred to as “digital ageism”. Its objective is to offer EU legal perspective on digital ageism in the context of AI in medical decision-making. It starts by laying out the ethical and legal concerns of ageism in healthcare. It explains how ageism manifests in the design and use of AI medical devices. Then, it assesses the EU legislative approach to medical AI, specifically the MDR and the AI Act. It concludes that, while the EU legal framework does address the key issues related to technical biases in medical AI, it does not account for contextual biases (e.g. when a medical treatment requires the use of a mobile device and digital literacy of older patients is not considered in the deployment of the AI tool).
I highly recommend reading the full article here to familiarise with this very important issue.
💺 AI in healthcare: Buckle up for change, but read this before takeoff
News
This article is part of the World Economic Forum that just concluded last week. It recaps some actions needed by the players in healthcare to fully embrace the AI era in the field, and some recent developments. The US National Academy of Medicine’s has been working on the Code of Conduct for AI aiming to provide a framework to ensure that AI algorithms and their application in health, healthcare and biomedical science perform accurately, safely, reliably, equitably and ethically in the service of better health for all. You can find the list of the five main goals. The article summarises some of the challenges healthcare is facing, such as staff shortages, healthcare access, and excessive cost, and mentions some recent developments in AI to tackle some of these shortcomings. Mayo Clinic, Philips and Google are highlighted as examples of organisations that have taken the initiative to set some guidelines and principles for responsible technology.
💊 Using AI, MIT researchers identify a new class of antibiotic candidates
News
In a recent study published in Nature, a group of MIT researchers used a deep learning model to identify compounds that can kill a drug-resistant bacterium that causes more than 10,000 deaths in the United States every year. The compounds also show very low toxicity against human cells, making them particularly good drug candidates. A key innovation of the new study is that the researchers were also able to augment the black box model to try to identify what it was using to make its predictions. This knowledge could help researchers to design additional drugs that might work even better than the ones identified by the model. The work is part of the Antibiotics-AI Project at MIT.
The study is open access and can be found here.
🥳 I hope you enjoyed the findings!
We are back with our bi-weekly spotlight with AI Health Hub.
✨ Do you have any predictions for AI in healthcare for this year? Would you like to see more specific contents in our newsletter?
Feel free to leave us a comment with some of your thoughts, and share with your network. 😁
Great read, thank you! It's important to read about our impactful paradigm shift.
We are all witnessing how AI-driven diagnostics and personalized treatment plans are revolutionizing healthcare.
In the dynamic landscape of healthcare, the fusion of artificial intelligence and medical innovation is redefing diagnostics and treatment strategies more and more. As we embrace this transformative era, the spotlight is on the substantial improvements expected in AI-driven diagnostics accuracy and the ushering in of personalized treatment plans.
Machine learning algorithms are now capable of swiftly analyzing vast datasets, identifying subtle patterns, and making accurate predictions. This capability extends across various medical fields, from radiology to pathology, promising a new era of precision diagnostics.
By leveraging AI, healthcare professionals can anticipate more accurate and timely detection of diseases. For instance, in radiology, AI algorithms excel at identifying abnormalities in medical imaging, potentially reducing human error and expediting the diagnosis process. This advancement not only enhances diagnostic reliability but also allows for early intervention and improved patient outcomes.
The paradigm shift toward personalized medicine gains momentum this year, driven by AI's ability to analyze patient-specific data comprehensively. Treatment plans tailored to an individual's genetic makeup, lifestyle, and unique health profile are becoming increasingly feasible. This personalized approach holds the promise of optimizing therapeutic outcomes while minimizing adverse effects.
When we consider oncology, AI algorithms are at the forefront of identifying genetic markers and molecular characteristics that dictate a patient's response to specific treatments. This knowledge allows healthcare providers to prescribe targeted therapies, increasing the likelihood of successful outcomes and reducing unnecessary side effects.
While the prospect of AI-driven diagnostics and personalized treatment plans is exciting, it comes with its set of challenges and ethical considerations. Ensuring data privacy, addressing algorithm biases, and maintaining transparency in decision-making processes are critical aspects that demand ongoing attention. As we embrace these advancements, it is imperative to navigate the ethical landscape responsibly.
As for healthcare communications, the quest for more detailed insights on the best AI applications in social media is paramount. A closer examination of how AI mediums can effectively disseminate information, engage with audiences, and foster community involvement holds the key to maximizing the impact of healthcare advancements.
2024 promises a revolutionary chapter in healthcare, driven by the synergy of AI and medical expertise. The strides made in AI-driven diagnostics accuracy and personalized treatment plans herald a future where healthcare is not just reactive but proactive and tailored to the unique needs of each individual. Embracing these advancements with a mindful approach to ethical considerations ensures a transformative journey towards a healthier and more personalized future.
It would be interesting in future newsletters to dive deeper into the intersection of AI and social media which can provide valuable insights for healthcare professionals, researchers, and enthusiasts alike.