Authors: Sara Riggare, Jamie Luckhaus, Therese Scott Duncan, Uppsala University, Sweden

Reviewed by: Ali Saad, PhD, Anigma Technologies.Mónica M. Kurtis. MD, Movement Disorders Unit Director,Hospital Ruber Internacional, Madrid, Spain

Every person with Parkinson’s disease (PD) experiences a unique journey – some people’s symptoms progress slowly over many years, while others may develop severe disabilities within a shorter time. “What is going to happen to me?” This is often one of the first questions a person diagnosed with Parkinson’s disease asks. The idea of using artificial intelligence (AI) to predict the course of an individual’s PD is compelling. In theory, machine learning algorithms could analyze large datasets – symptoms, genetic markers, brain scans, lifestyle factors – and identify patterns to forecast how fast a patient’s condition might worsen or which symptoms might emerge. Such predictions could help patients and doctors plan treatments and life adjustments in advance. However, despite progress in AI and data collection, accurately predicting PD progression remains an enormous challenge, and it raises important practical and ethical questions we must confront before clinical use.

The appeal of prognostic algorithms

Researchers are utilizing studies like the Parkinson’s Progression Markers Initiative (PPMI), which collect data from thousands of patients, to train AI systems that separate patients into groups with different progression. Tech companies, including IBM with support from the Michael J. Fox Foundation, used PPMI  to build models that may help identify early signs of aggressive disease by descovering data-driven disease state and support more personalized care [1].

AI is also making it easier to remotly track symptoms between clinic visits. At UC San Francisco, researchers created a system that uses smartphone video to analyze movements like walking or finger tapping [2]. This tool produces objective scores that can reveal subtle changes over time and confirm whether treatments are working. Such data-driven monitoring could help doctors adjust medications earlier, and providing more personalized care.

Other projects use “passive” tracking, such as monitoring breathing during sleep. In one Nature Medicine study, researchers developed an AI model that analyzed signals from a simple belt or contactless sensor and assessed Parkinson’s severity as accurately as a neurological exam [3]. This technology requires no effort from patients, can be used at home, and may make expert care accessible even in remote areas.

Beyond monitoring, some AI models are being trained to predict outcomes like memory decline. One PPMI-based  model combining brain scans with clinical measures predicted significant cognitive problems within five years with about 89% accuracy [4]. Other models forecast motor changes, such as when a patient might need walking aids.

No biomarker, no crystal ball

Despite these advances, there is a fundamental scientific hurdle: PD still lacks a clear and validated biological marker of progression. Unlike, say, cholesterol for heart disease or viral load for HIV, there is no single blood test, brain scan, or measurement that definitively tracks how Parkinson’s is advancing. Doctors mainly rely on clinical rating scales (like the UPDRS), observed milestone events (e.g. needing a wheelchair), or composite scores to gauge progression – all imperfect proxies. This makes training an AI tricky: the “ground truth” it tries to predict is hard to define and measure consistently. Different studies may label progression differently, and patients vary widely in symptom patterns. An AI might learn to predict a certain outcome (for example, onset of dementia or rate of motor decline), but that outcome may not capture every individual’s experience of “progression”.

Moreover, models developed so far are not yet ready for clinical use. Machine learning models from PPMI data show promise but are still in research phases. They may work well on the dataset they were trained on, but could perform less accurately in the real-world clinic with more diverse patients. In short, AI predictions of PD progression are still experimental. People understandably want to know how fast their disease will progress, but the fact is that it is nearly impossible to predict how any individual person’s PD will evolve, and neurologists often refrain from answering these questions.

Do people want to know the future?

Suppose for a moment that we did have a highly accurate PD progression predictor – the next question is, do patients want this information? The answer isn’t straightforward. Many patients do ask about their prognosis, as it could help with life planning and setting expectations. Knowing one might face significant disability in, say, five years could influence decisions about retirement, finances, or bucket-list goals. And if a slower course is predicted, it could provide peace of mind. There is value in knowledge.

On the other hand, Parkinson’s is a case where detailed prognostic knowledge can be a double-edged sword. Not everyone may want to peer into that crystal ball. Parkinson’s advocate Sara Riggare reflected that had she been told as a teen that she had an “old person’s disease” like PD, she likely would have avoided pursuing university, a career, or starting a family – decisions which, in hindsight, she’s glad she made despite the diagnosis. Knowing the worst-case scenario early on might have curtailed her ambitions unnecessarily. Another person with PD, columnist Sherri Woodbridge, observed that none of her neurologists ever spelled out how bad Parkinson’s could get – and she isn’t sure she would have wanted them to. “I was doing quite well getting [to a dark place] on my own” with fear, she writes, “I didn’t need my neurologist to assist me further into the deep dark” (https://parkinsonsnewstoday.com/columns/knowing-preparing-what-future-holds/). Crucially, she notes doctors aren’t clairvoyant; any prediction is essentially an educated guess. “They don’t have the ability to tell your future… We think we want them to tell us what our future holds. Or do we?”. Her perspective highlights the emotional burden that a prognosis – especially a probabilistic one – can bring. It could cause anxiety or despair “borrowing trouble from tomorrow”, especially given that not every patient will  experience all potential symptoms of PD.

Thus, even if an AI could estimate that someone is a “fast progressor,” delivering that news must be done with great care and with the patient’s desires in mind. Some people will prefer to know all information, while others might choose not to hear grim predictions that might never fully materialize. This is analogous to genetic testing situations (for example, the choice to know one’s risk of Huntington’s disease or Alzheimer’s); personal preference is paramount.

Looking ahead: cautious optimism

The ultimate vision is that AI-driven prediction tools will enable a proactive approach to PD management. Instead of a reactive model (waiting for significant symptom worsening before changing therapy), doctors and patients could anticipate changes and act early. For example, an AI might forecast that based on current trends, a patient’s walking ability will significantly decline within the next year – prompting the care team to intensify physical therapy and implement fall-prevention strategies now, rather than after falls occur. For patients, having an idea of their likely future could guide personal decisions and coping strategies, although it’s always important to remember that these predictions are probabilistic, not guarantees.

AI and big data will undoubtedly play a growing role in understanding PD. They might help uncover hidden patterns or subsets of PD that respond differently to treatments. In the future, if disease-modifying therapies become available, predicting progression could help target those therapies to the patients who need them most urgently. For now, though, AI-based progression prediction is in its infancy, and both patients and clinicians should view any such forecasts as tentative. The lack of a definitive biomarker and the variability of PD mean that current models have wide margins of error.

Patients can take heart that there are still plenty of ways to influence their journey with PD, even without a predictive algorithm. While we can’t yet reliably forecast the course of someone’s Parkinson’s, we do know that steps like regular exercise, a healthy diet, staying socially and mentally active, and adhering to treatments can improve daily functioning and potentially slow symptom progression. These are things every patient can focus on, with or without AI.

In conclusion, the vision of an AI crystal ball for Parkinson’s is exciting and not science fiction – research is underway and making headway. But until we address challenges like establishing reliable progression markers and ensuring predictions truly benefit patients’ well-being, such tools should complement, not replace, the individualized care planning done by patients in conversation with their doctors. And any information about the future of one’s illness must be handled with compassion, consent, and context. As with many aspects of healthcare, technology will be most useful when guided by the needs and preferences of the human being at the center. In Parkinson’s, the human element – the resilience, support, and proactive self-care of patients – remains as crucial as ever, whether or not we can predict the road ahead.

References

[1] Severson, K. A. et al. Discovery of Parkinson’s disease states and disease progression modelling: a longitudinal data study using machine learning. Lancet Digit Health 3, e555–e564 (2021). https://doi.org/10.1016/S2589-7500(21)00101-1

[2] Deng, D. et al. Interpretable video-based tracking and quantification of parkinsonism clinical motor states. npj Parkinsons Dis. 10, 122 (2024).

[3] Yang, Y. et al. Artificial intelligence-enabled detection and assessment of Parkinson’s disease using nocturnal breathing signals. Nat Med 28, 2207–2215 (2022).

[4] Gorji, A. & Fathi Jouzdani, A. Machine learning for predicting cognitive decline within five years in Parkinson’s disease: Comparing cognitive assessment scales with DAT SPECT and clinical biomarkers. PLoS ONE 19, e0304355 (2024).