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AI is revolutionising medical science. It's a doctor's friend, but conditions apply

  //science-technology.news-articles.net/content/2 .. it-s-a-doctor-s-friend-but-conditions-apply.html
  Published in Science and Technology on Saturday, September 6th 2025 at 10:23 GMT by ThePrint
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AI is Revolutionising Medical Science – but Conditions Apply

The promise of artificial intelligence (AI) to transform healthcare is a narrative that has dominated headlines, policy debates, and even bedside discussions in recent years. A new feature in The Print explores this narrative in depth, arguing that while AI is rapidly becoming a “doctor’s friend,” its benefits are not automatic and come with a host of caveats that must be addressed if the technology is to live up to its hype.

The New AI‑Enabled Diagnostic Toolbox

One of the most tangible ways AI is reshaping medicine is in diagnostics. The article points to the FDA’s 2021 approval of an AI‑driven radiology assistant that flags pulmonary nodules on CT scans—an innovation that has cut the time clinicians spend on image review by a significant margin. It also cites the use of computer vision in pathology labs, where algorithms can flag early-stage cancers in biopsy samples faster and sometimes more accurately than a human pathologist. Beyond imaging, the piece discusses AI’s role in retinal screening: an algorithm that can read retinal photographs and flag diabetic retinopathy with an accuracy that rivals ophthalmologists.

“These are early, concrete examples where the machine has outperformed or at least matched human expertise,” the article notes, quoting Dr. Ananya Gupta, a radiologist in Mumbai who has integrated an AI tool into her workflow. “That’s a win, but it’s not the whole story.”

Breakthroughs in Drug Discovery

The article also dives into AI’s influence on drug discovery. It references DeepMind’s AlphaFold, which solved the protein‑folding problem with unprecedented accuracy. While AlphaFold is not yet a drug, the ripple effect on pharmaceutical research has been dramatic: researchers can now model the structure of viral proteins—like those of SARS‑CoV‑2—much faster, speeding up vaccine design and antiviral development. The piece mentions a partnership between pharmaceutical giant Novartis and AI firm Insilico Medicine that uses generative models to predict novel drug molecules, reportedly shortening the pre‑clinical phase by up to 60%.

The “Conditions” – When the Friend Becomes a Foe

Despite these successes, the article emphasizes that AI is not a panacea. Three major conditions—data quality, human oversight, and regulatory frameworks—must be in place for the technology to deliver on its promise.

1. Data Quality and Bias

AI models learn from data, and if that data is unrepresentative or flawed, the models inherit those biases. The article cites a 2020 study that found an AI algorithm used for risk‑stratification in cardiac care was less accurate for Black patients, largely because the training set under‑represented that demographic. The Print piece urges that before deploying AI in a clinical setting, institutions must audit datasets for diversity and fairness. It also warns that proprietary data silos can limit external validation, making it difficult to know how an algorithm will perform in a different population.

2. Human Oversight and Explainability

“Doctors are still the ultimate decision makers,” says Dr. Gupta in the article. “AI should be a tool, not a replacement.” The piece highlights the “black‑box” problem of deep learning models, which can produce highly accurate predictions yet offer little interpretability. This opacity can erode clinician trust and impede patient acceptance. The article cites the FDA’s own “Explainable AI” initiative, which encourages manufacturers to provide clear information on how their algorithms reach conclusions, especially for high‑stakes decisions like cancer screening.

3. Regulatory and Ethical Landscape

The article details how regulatory bodies are still grappling with how to evaluate AI as a medical device. While the FDA has cleared a handful of AI algorithms, the pathway is still uneven. The European Union’s proposed AI Act is set to impose stricter rules on high‑risk AI in healthcare, including rigorous testing and post‑market surveillance. The article stresses that until a global regulatory framework is in place, there is a risk of fragmented approvals that could hamper international research collaborations.

Ethical Considerations and the Digital Divide

Beyond technical pitfalls, the article argues that AI can inadvertently widen health disparities. If AI tools are only available in well‑funded urban hospitals, patients in rural or low‑income settings may be left behind. The Print also calls attention to data privacy concerns: massive datasets that feed AI models often contain sensitive health information, and the line between data use for the public good and commercial exploitation can be blurry.

Looking Ahead: The Need for a Multi‑Stakeholder Approach

The article concludes that the full realization of AI’s potential will require a partnership between technologists, clinicians, regulators, and ethicists. It calls for open‑source initiatives that encourage external validation, the establishment of standardized datasets, and the development of “AI‑audit” frameworks that can be applied across institutions.

It also stresses the importance of patient education. “Patients need to understand what AI can and cannot do,” the piece writes. “Only then can they make informed consent choices about having an algorithm involved in their care.”

Key Takeaways

Final Thought

AI is undeniably reshaping modern medicine. Its algorithms are already reading scans, predicting disease trajectories, and suggesting new therapeutics. Yet the article reminds us that this “friend” requires careful cultivation. Without robust data stewardship, transparent algorithms, and thoughtful regulation, the technology risks becoming a double‑edged sword. As the Print aptly puts it, “AI is a powerful ally, but it is not a silver bullet—conditions must apply.”