Recent reports about Moderna and Merck’s personalised mRNA vaccine for melanoma have renewed claims that artificial intelligence could help cure cancer and other diseases within the next decade. The vaccine has shown significant success in reducing melanoma recurrence in Phase 3 trials, but describing it primarily as an AI-driven breakthrough risks overstating the role of the technology.

AI may have been used during the development process, but there is limited evidence that it played a decisive role in creating intismeran autogene, the personalised melanoma vaccine. The development has nevertheless added momentum to a growing debate in Silicon Valley, where technology leaders increasingly argue that AI could dramatically accelerate medical research and potentially lead to cures for many diseases within years.

Silicon Valley’s Growing Confidence In AI

The Moderna and Merck development comes shortly after Anthropic CEO Dario Amodei predicted that AI could help cure most human diseases, including cancers, within the next five to 10 years.

“I think it will actually be possible to cure most human diseases in 5 to 10 years, as crazy as it may sound to ordinary people and frankly to biologists as well,” Amodei wrote last week.

Amodei linked his optimism to a personal experience, saying his father died from Hepatitis C only a few years before direct-acting antiviral medicines such as sofosbuvir were developed. Those treatments can cure around 95 per cent of patients, he noted.

His prediction received support from parts of Silicon Valley, but drew criticism from doctors and researchers who argued that medical breakthroughs do not automatically translate into widespread cures.

Musk And Hassabis Back AI’s Medical Potential

Elon Musk also supported Amodei’s prediction, writing that “AI will do it” in response to the debate over whether artificial intelligence could eliminate most diseases, including cancers, within the next few years.

Following reports about the Moderna vaccine, Musk added: “Artificial RNA essentially makes curing diseases a software problem. Synthetic RNA will cure many diseases. So many breakthroughs are coming.”

The optimism echoes views expressed for years by Demis Hassabis, the Nobel Prize winner and former Google DeepMind CEO. Hassabis has played a prominent role in AI-based protein folding research, an area considered important for understanding biological processes and developing potential medicines.

Hassabis has repeatedly argued that AI could lead to major advances against diseases, including cancer. Around 10 days ago, he told The Times in London that he was now highly confident such breakthroughs would occur within the next decade or two.

Doctors Agree AI Could Accelerate Research

Oncologists and other medical specialists broadly recognise the potential of AI but are far less certain about the timelines predicted by technology executives.

Dr Garvit Chitkara, Oncosurgeon and Associate Director of Breast Surgical Oncology and Oncoplasty at Nanavati Max Institute of Cancer Care, Mumbai, said AI could become a powerful tool for medical research because of its ability to process biological and clinical information at a scale beyond human capacity.

“AI has the potential to become one of the biggest accelerators of medical science because it can analyse biological and clinical data at a scale that humans simply cannot,” Chitkara said.

However, he rejected the idea that most diseases could be cured within five years.

“I do not think that most diseases will be cured within five years, because biology does not work on a fixed timeline,” he said.

From AI Discovery To Medical Treatment

Dr Mandeep Singh Malhotra, Director of Surgical Oncology at The CK Birla Hospital, Delhi, also described AI’s potential as enormous but cautioned against treating ambitious forecasts as established timelines.

“AI has enormous potential to accelerate medical research, but the idea of curing most or all diseases within 5 to 10 years is highly ambitious,” he said.

Malhotra pointed to the stages that must follow a scientific or computational discovery before it can become a treatment. These include laboratory validation, clinical trials, regulatory approval and long-term monitoring for safety.

“I would therefore view the 5 to 10-year prediction as a powerful vision rather than a guaranteed timeline,” he said.

Biology Remains AI’s Biggest Challenge

Dr Sandeep Nayak, Chairman of Oncology at MACS-Renova Oncology Institute, KIMS Hospital, Bengaluru, said the complexity of biology makes it difficult to apply the rapid progress seen in other AI fields directly to medicine.

“Not realistic on that timeline,” he said. According to Nayak, one of the major limitations is the quality and quantity of biological data available to researchers.

“Machine learning works best on well-specified questions with abundant, high-quality data, curing diseases is not that,” he said.

Unlike many problems where AI can work with large and structured datasets, diseases can involve complex interactions between genetics, biology, environment, individual physiology and treatment response.

Some Doctors Remain Optimistic About AI

Not all specialists are equally sceptical about the predictions coming from Silicon Valley. Dr Aanchal Bhatia, Orthopaedic Oncologist at Medicover Hospitals, Bengaluru, said she was optimistic about what AI could achieve over the next five to 10 years.

Bhatia said AI could accelerate medical research, improve early disease detection, deepen understanding of disease biology and help identify potential treatments more rapidly.

However, she also distinguished between accelerating research and actually curing diseases.

“Whether that translates into curing most diseases is difficult to predict because biology is complex. But AI could compress years of research and analysis into a much shorter period,” she said.

The debate therefore remains less about whether AI will influence medicine and more about how quickly that influence can translate into safe and effective treatments. While technology leaders see the possibility of dramatically shortening the path from discovery to cure, oncologists emphasise that clinical testing, biological complexity and long-term safety remain significant hurdles.

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