We are entering one of the most revolutionary periods in the history of medicine and biomedical science. The convergence of Artificial Intelligence (AI), machine learning, genomics, precision medicine, robotics, and advanced medical microbiology is rapidly transforming healthcare from a reactive system into a predictive, personalized, and data-driven discipline. From neonatal intensive care units to infectious disease surveillance and cancer therapeutics, emerging technologies are reshaping how diseases are diagnosed, monitored, prevented, and treated. As healthcare systems face increasing pressures from antimicrobial resistance, global pandemics, aging populations, and inequitable access to care, the integration of AI into biomedical research and clinical medicine may represent one of the most significant scientific turning points of the 21st century.

In medical microbiology, AI-powered diagnostic platforms are already demonstrating the potential to detect pathogens faster than traditional laboratory methods through genomic sequencing, automated imaging, bioinformatics, and predictive analytics. Machine learning algorithms can analyze vast microbiological datasets to identify antimicrobial resistance patterns, forecast outbreaks, and support precision antimicrobial stewardship. Similarly, in neonatal and critical care medicine, AI-assisted monitoring systems are being explored for the early prediction of sepsis, respiratory compromise, and clinical deterioration before overt symptoms emerge. These innovations may dramatically reduce mortality while improving clinical decision-making and resource allocation in both high-income and resource-limited healthcare settings.

Beyond diagnostics, the future of medicine is increasingly moving toward precision and individualized healthcare. Advances in molecular biology, omics technologies, immunology, and computational medicine now allow researchers to explore disease at genetic, cellular, and metabolic levels with unprecedented detail. The integration of AI with biomedical science may accelerate drug discovery, improve vaccine development, optimize clinical trials, and uncover novel therapeutic targets faster than ever before. Future healthcare systems may rely on real-time patient data, wearable biosensors, digital twins, and predictive models capable of identifying disease risk before clinical onset. This shift has the potential to redefine preventive medicine and transform patient outcomes globally.

However, alongside these remarkable opportunities come major scientific, ethical, and public health questions. Can AI truly replace aspects of clinical reasoning, or should it remain a supportive tool for clinicians and scientists? How do we address algorithmic bias, patient privacy, and unequal technological access between healthcare systems? Will future biomedical scientists require computational and data science expertise alongside traditional laboratory training? Most importantly, how can the global scientific community ensure that these innovations benefit all populations rather than widening existing healthcare disparities?

As biomedical science enters the era of intelligent medicine, interdisciplinary collaboration between clinicians, microbiologists, biomedical scientists, data scientists, engineers, and public health experts will become increasingly essential. The future of medical research may not depend solely on technological advancement, but on how effectively science, ethics, innovation, and equitable healthcare delivery can evolve together. The next generation of biomedical professionals will not only diagnose disease, they may help design the intelligent healthcare systems that redefine the future of humanity itself.

This prompt to write a case study as applied to neonates. At the end ( sources ) you'll find the last article on the use of deep learning to predict outcomes

In the last decade, many different projects around the globe have attempted to use artificial intelligence to improve outcomess of premmies, estimate age, and predict the possible complications in the NICU stay.

Several ethical issues and how the neonatal community is trying to use it for the good. Each premature infant generates a huge amount of data.  This is where AI comes in bringing insight, but with an ethical implication beyond data privacy with many ramifications, extending data breaches to threaten health disparities due to population sample usesd to train AI models. Thus,

This Health data mining has a huge potential for harm.

In past, there have been many attempts by tech companies to monetize this data.

In the early 2000  with the advent of genomics legislators tried to protect the user by passing laws such as Gattaca, to prevent discrimination.  This was not enough as tech usually is ahead of legislation.

Starting with the bad. Here is an example that struck me the most as explained by the “ website.

A recent ethical misconduct case illustrated by David Marco is Google’s “project Nightingale”, where they were able to access personal health records to develop their AI. In doing so, violating several ethical principles starting with consent without asking for consent from the patient and without notifying medical professionals.Secondary violations in terms of data privacy and transparency about the objective of the project.  The company claimed they respect the HIPAA legislation business clauses. In my opinion, clearly not enough and not acceptable. In addition, breaking Dr. Jagadish's first rule - do not be surprised.

When I heard the episode  “Harnessing the Power of AI for Better Neonatal Outcomes (ft NeoMIND-AI founders Drs. Barry and McAdams)” I was intrigued but skeptical due to data privacy issues, discrimination and equity ramifications. After listening to the @nicupodcast episode It hits a lot of good points about how tech can bring better care to the NICU, in terms of insights and capabilities augmented for clinicians. Yet it still feels it needs care and consciousness. Here there are data privacy concerns, so we need to take care with parental consent.  In addition,  Algorithms need to be tweaked to prevent discrimination against minorities.  The doctor also explains that data sharing is tricky in terms of ethics.

https://podcasts.apple.com/es/podcast/the-incubator/id1566031191?i=1000655297640

To conclude that AI needs to be used with consciousness, transparency, solid data validation and engaging the stakeholders such as parents and clinicians.

Sources /
Data Ethics: Master Responsible Data Use: https://www.ewsolutions.com/understanding-data-ethics/

The ethics of data mining in healthcare: challenges, frameworks, and future directions - PMC

AI can predict preemies’ path, Stanford Medicine-led study shows

Swedish Television recently broadcasted two programs about "the Miracle Kids", an investigative journalistic approach. We do have a national guideline in Sweden, advocating that neonatal intensive care can be considered at this gestational age (here in Swedish, Google translate will help to get this in your local language). The take-home message from this coverage by Swedish Television are that 1) most 22-weekers are offered intensive care, 2) mortality is high still and adverse longer-term outcomes are very common in survivors, and 3) the societal support for those families after NICU discharge (even in Sweden!) is poor.

Here are the episodes #1 and #2, but in Swedish. I have suggested Swe Television that they offer English subtitles, but yet not. Maybe some smart AI or translation service can help!

Today another interesting paper surfaced on my radar, US data from VON, reporting mortality, and discharge with/without complicartions and tech dependency. The Figure below from the paper gives a good summary.

It would be very interesting to hear your experiences and reflections about the difficult questions around 22-weekers. The tv-programs have sparkled a lot of professional discussion in Sweden, and the need for structural followup and societal support etc.

But I think there are Big Elephants in the (delivery) room is really, what are the right, reasonable and wrong strategies around 22-week deliveries.

Before I share my own thoughts, would be great to hear from you!

Most countries have implemented newborn screening and with affordable genetic testing available, there have been studies looking into screening with genetic tests. But what should be tested for? How do we handle markers for diseases that occur later in life? How do we deal with the emotional aspects for parents, children and care givers? And how do we deal with requests from third parties?

https://www.nature.com/articles/s41591-024-03227-9

Latest article I discovered relating to Japanese care neonatal of the tiniest babies 22-25. Good 🌟 news on the positive outcome. It is worth a 📖.

It is an open access article published by Tokyo University researchers in nature September 2024 #FOAM #neonatal #beyondlimits #periviability #neonatalethics

https://www.nature.com/articles/s41372-024-02093-0

A panel of neonatology experts have now reviewed the (legal) evidence and found that (medical) evidence speaks for alternative and natural causes of death.

I find it hard to fully grasp the whole situation, first of all, if someone being unguilty is declared guilty, that is disastrous at so many levels, from the imprisoned person to the society as a whole. Calling this "Epic fail" is not enough.

I wanted to share a comment by Prof Neena Moody in London, a piece to the point.

What do you think and feel around all this?

https://www.theguardian.com/commentisfree/2025/feb/12/lucy-letby-case-trial-justice

It is about how to approach infants born at the viability limit (i.e. 22 weeks). The article is in Swedish, but Google translate may help of you are interested to read through. https://www.svd.se/a/mQ929l/hugo-lagercrantz-hur-pass-tidigt-fodda-ska-vi-forsoka-radda

Prof Lagercrantz's takehomes are:

• NICU care and post-NICU care of these infants requires a national strategy
• in addition to health care providers (doctors specifically!), philosophers, theologists, paralegals and brain researchers should contribute in writing such a national strategy

I would like to add, parents should also be included in such a process!

As of now in Sweden, we have a national guideline from 2016 about referral to university hospitals from 22+0 and onwards. I would argue this document is a good start, but it lacks several aspects, for example when/how to withhold or withdraw care.
This document is currently being revised, hope it will be communicating a wider approach to this difficult question.

Would be great to learn how you base decision at the border of viability? Who decides what and when?

Share your views in this 10-minute survey. 

We are seeking the views of English-speaking paediatric or neonatal intensivists (either practising or in training) on the use of rapid genomic sequencing for critically ill infants. Our short survey will ask about two different ethical scenarios surrounding gaining consent for genomic testing and how a genomic test might influence treatment decisions. 

This will be the first study to collect global intensivist perspectives on this topic.

If you are able to participle, please click on the following link:
https://redcap.mcri.edu.au/surveys/?s=HTP88W4FHLA483F8

Please feel free to forward this ad to relevant colleagues!

Any questions, please email: biomedicalethics@mcri.edu.au

Christopher Gyngell
Senior Lecturer, Department of Paediatrics, The University of Melbourne
Team Leader, Biomedical Ethics Research Group, Murdoch Children’s Research Institute

Following completion of aEEG tracing how are you storing the information. We used to physically download onto disc, but with onset of flash drives wondered how it was now being stored given it’s potentially Medico legal ramifications. TIA. Please keep safe.

Kind regards 

Alistair