"Ending 'Invisible Concussions'? The Impact of AI That Determines in Seconds Just by Voice"

"Ending 'Invisible Concussions'? The Impact of AI That Determines in Seconds Just by Voice"

"Concussions are invisible." Those involved in sports should be well aware of the weight of these words. Falls, contact, collisions—even if an athlete can stand up immediately afterward, it's not easy to determine if they are truly okay. CT scans and other imaging tests often fail to capture concussions, and in reality, on-site assessments tend to rely on brief interviews and self-reporting.


In response to this "difficulty in judgment," a research approach that tackles the issue with the idea of "if you can't see it, listen for it" is gaining attention. This voice-based evaluation method involves AI analyzing subtle changes in voice to indicate the possibility of a concussion within seconds.


The trigger was a decision made on the field that "went back"

The article begins with an incident that occurred in the NFL in 2022. It describes how player Tagovailoa returned to play after a head injury, but the league later acknowledged that it should have been treated as a concussion. The decision on the field to treat it as a "back injury" rather than a neurological issue is emblematic.


Of course, even if rule changes have progressed since then, unless there are more reliable means to make decisions "at that moment," similar uncertainties will be repeated. This is where the research aims to make a difference.


Not a "voice fingerprint," but a "voice health meter"

The research team focused on "speech biosignatures." Literally translated, it means "biological signs of speech." While voices are often compared to fingerprints due to their individuality, the important point is that a voice is not a fixed fingerprint; it can change due to health conditions, injuries, or even intoxication.


In other words, by comparing "normal voice" with "post-injury voice," changes occurring within the individual may become apparent. AI can detect differences at a level that human ears cannot perceive, capturing them as features.


How data is collected and what changes were observed

According to the article, the research group collected voice samples from numerous athletes at the high school and college levels, both before the season (baseline) and during the season (including post-injury if necessary). Sports such as boxing, tackle football, lacrosse, rugby, and cheerleading were among those targeted.


They compared cases where concussions were confirmed with a control group without concussions. It was shown that there were differences in indicators such as amplitude, frequency (pitch), and vibration. Changes that might be dismissed as "imaginary" by the human ear can be captured as patterns by AI.


Furthermore, advancements in machine learning have been a tailwind, and current tools are reportedly at a stage where they can correlate voice changes related to brain injuries with "over 90% accuracy."


The key to practical application is a system that can be completed "in a single word"

However, considering on-site implementation, the more complex the examination, the more difficult it is to operate. Therefore, the research is progressing towards narrowing down the traditional procedure of speaking multiple words, phrases, or sounds to fewer elements—ideally "a single word or specific sound."


If this direction is realized, a system could be envisioned where the baseline of all players is obtained and stored on tablets, etc., during the pre-season, and the same speech is immediately compared right after an accident or contact occurs. The results could be shown in forms such as "mild, moderate, severe," serving as a basis for decisions on rest and return processes.


The significance of "understanding through voice"—filling the gap of oversight

The article also presents estimates that in the U.S., "over half of concussions are undiagnosed," and about 70% of occurrences are in sports environments.


If this is close to the truth, the problem is not so much "a lack of diagnostic technology" as it is "a weak system for capturing issues on the field."


The value of AI voice evaluation lies precisely in this "capturing" scenario. It is quick, involves relatively light equipment burden, and is less dependent on the ambiguity of self-reporting. Of course, it is not meant to replace the final diagnosis but to function as a screening that "strengthens or weakens suspicion," which can change on-site decision-making.


While expectations are growing, caution is also necessary

The research side is also interested in the application range, including the evaluation of neurological diseases like Parkinson's and Alzheimer's, where acoustic characteristics of speech change with progression. Additionally, there is mention of potential use in high-risk occupations such as police, fire, military, and construction, beyond sports.


However, there are points of discussion that inevitably arise in social implementation.

  • Cost of Misjudgment: Both false negatives (misses) and false positives (overdiagnosis) affect on-site decision-making.

  • Operational Design: "Who handles AI results and how?" The responsibility structure for return decisions and alignment with league or school regulations.

  • Privacy: Voice is highly personal information. Concerns about how baselines are stored, restrictions on third-party provision, and misuse for purposes other than intended.

  • Baseline Issues: Resistance to general environments where pre-recording is not possible, or situations where voices change daily (colds, fatigue, noise).


The essence of the research lies in the point that "voices change," but precisely because of this, how to handle "changes other than injuries" becomes important on both technical and institutional fronts.


Reactions on Social Media (Commonly Seen Points and Phrasing Trends)

*Since this topic is relatively new, rather than a single post having gone viral, common reaction patterns in related topics (concussions, sports medicine, AI voice analysis) are introduced.*


1) Expectations: "This will reduce oversights"

  • "Having objective indicators right after a fall is powerful."

  • "It seems effective for the issue of athletes who don't self-report."

  • "If it can be used in school clubs and amateur settings, its value is significant."

The background includes an awareness of the problem that short interviews and visual evaluations have their limits.

2) Caution: Criticisms of the "over 90%" presentation

  • "‘90% accuracy’ depends on conditions. What about noise on-site or dialects?"

  • "If positive, immediate substitution? Who makes the final decision?"

  • "The challenge is more in the design of the operation (protocol) than in accuracy."

In AI for medical and safety fields, the impact of numbers tends to precede, drawing attention to "prerequisites," "reproducibility," and "responsibility after implementation."

3) Privacy: "Voice data is strong personal information"

  • "Who holds the voiceprint-like information?"

  • "Won't it be misused for insurance or employment?"

  • "It's scary if sports organizations collect it almost compulsorily."

As explained in the article with the concept of "voice biosignatures," voice is highly personal data. This tends to attract sensitive reactions.

4) Sports Context: "To prevent repeating that case"

  • "Ultimately, the pressure from the field to put players back is the problem."

  • "More than technology, it's about whether the rules that should be protected are being followed."

  • "A system is needed to prevent 'convenient interpretation' of AI results."

There is a perspective that the more technology is introduced, the more the "politics of judgment" becomes visible.


Sources

Related Articles