2024 mein 70 se zyada health AI companies ko FDA clearance mili. Clinical AI mein duniya bhar ki investment $15 billion se upar chali gayi. Har bare health system ne apni AI strategy ka elaan kiya.
Aur US ke hospitals mein baghair kisi warning ke bigarne wale mareezon ki tadaad mein koi khaas farq nahi aaya. Chronic kidney disease pakarne mein ab bhi saalon lag jate hain. Outpatient settings mein diagnostic errors ab bhi har saal andazan 12 million American adults ko mutasir karte hain. Aap ka naya doctor ab bhi aap se kehta hai ke apni dawaiyon ki list khud yaad karein.
Approach mein kahin kuch ghalat hai.
Healthcare ki zyada tar AI asal mein karti kya hai
Zyada tar clinical AI products notes ka summary banate hain, images classify karte hain, ya jab koi number threshold paar kare to alert chala dete hain. Sab kaam ke hain. Sab wrappers hain. Woh clinical data ko usi shakal mein lete hain jis mein woh pehle se hai, model se guzarte hain, aur ek nateeja samne rakh dete hain. Buniyadi data nahi badla. System mareez ko jis tarah represent karta hai, woh nahi badla.
AI ki madad se chest X-ray parhne wali radiologist ab bhi ek hi lamhe ki ek hi image parh rahi hai. Ho sakta hai AI woh nodule pakar le jo us se reh jata. Ye waqai qeemti hai. Lekin AI usay ye nahi bata sakti ke is mareez ka pulmonary function teen saal mein paanch tests ke dauran musalsal gir raha hai, ke aath mahine pehle ek dawai badalne ke baad ye girawat tez ho gayi, aur ye trajectory atharah mahine ke andar COPD ki taraf ja rahi hai. Ye maloomat maujood hai. Bas mukhtalif providers aur saalon mein bikhri hui hai. Koi wrapper us tak nahi pohanch sakta, kyun ke wrapper sirf maujooda encounter dekhta hai.
Taqreeban har clinical AI mein analysis ki unit ek visit hai. Ek note. Ek image. Insani bimari mein ye unit saal hain. Jab tak ye dono match nahi hote, healthcare mein AI ghalat sawalon ke jawab bas zyada tezi se deti rahegi.
Insani bimari mein analysis ki unit saal hain.
Speed ka jaal
Speed woh faida hai jis ka zikr har koi karta hai. Discharge note ka summary seconds mein. Physician baat khatam kare, utni der mein referral ka draft tayyar. Free text se diagnoses foran nikal lena.
Physician burnout haqeeqat hai. Documentation ka bojh kuchal dene wala hai. Jo cheez bhi clinical care ko waqt wapas de, us ki qadar hai. Lekin maujooda infrastructure par speed sirf efficiency ka masla hal karti hai. Jis physician ko aaj ki visit ka perfect AI summary mil jaye, usay phir bhi ye pata nahi ke pichle do saal mein teen providers ke paas us ke mareez ka creatinine barhta raha hai. Summary tez ho gaya. Clinical tasveer bilkul wahi hai.
Health systems woh AI apnate hain jo workflows tez kar de, productivity gains report karte hain, aur AI strategy ko kamyab qarar de dete hain. Is dauran bimari shuru hone aur pakri jane ke darmiyan ka faasla wahin ka wahin hai. System usi simt mein zyada tezi se chal raha hai. Kisi ne ye nahi poocha ke simt sahi bhi hai ya nahi.
Industry wrappers kyun banati hai
Wrappers banana samajh mein aata hai. Ek pre-trained model liya, clinical text par fine-tune kiya, aur chand mahinon mein product ship ho gaya. Validation aasan hai: note andar, summary bahar. Sales ki baat bhi jani pehchani hai: "Hum aap ke EHR mein plug ho kar usay smart bana dete hain." Data architecture par koi mushkil guftagu nahi. Workflow mein koi khalal nahi.
Waqt ke saath sehat ko parhne wali cheez banana bilkul alag kaam hai. Mareez ko saalon aur providers ke paar represent karna parta hai, aisi clinical reasoning banani parti hai jo position ke bajaye direction par chale, sainkron sites par validate karna parta hai, aur hospitals ko qaail karna parta hai ke kisi bimari ko do saal pehle pakar lena summarization plugin se kahin gehri integration ke qabil hai.
Is mein zyada waqt lagta hai, kharcha zyada hota hai, aur aisi guftagu karni parti hai jis ke liye zyada tar teams tayyar nahi. Lekin yahi wahid approach hai jo asal clinical khala ko address karti hai.
Black box masle ko aur bigar deta hai
Zyada tar clinical AI wrapper hone ke saath saath black box bhi hai. Model data leta hai aur ek output de deta hai. Physician reasoning nahi dekh sakti. Woh ye sawal nahi utha sakti ke kin inputs ne ye nateeja nikala. Woh beech ke qadmon par apni judgment nahi laga sakti, kyun ke beech ke qadam nazar hi nahi aate.
Amal mein clinicians in outputs ke saath wahi sulook karte hain jo EHR ke sainkron doosre alerts ke saath: shak ke saath, aur saalon mein pakki hui is aadat ke saath ke bas dismiss kar do. Opaque systems ka engagement musalsal 10 se 15 percent rehta hai. System alert deta hai. Clinician nazar andaz kar deta hai. AI chal rahi hai, lekin clinically be-asar hai.
Jin systems ne 80 percent se zyada clinician engagement hasil ki hai, un sab mein ek baat mushtarik hai: reasoning nazar aati hai. Clinician dekh sakta hai ke assessment kis buniyad par hua, aur us ke kisi khaas hisse se ikhtilaf kar sakta hai. Is ke baghair adoption hota hi nahi. Khud banana shuru karne se pehle main saalon tak yahi dekhta raha ke adoption nahi ho raha.
Asal farq dikhta kaisa hai
Ek mareez jis ka creatinine 18 mahine se do providers ke paas har quarter 0.1 barh raha hai. Kisi ek value ne alarm nahi bajaya. Har visit par number "within range" tha. Jo system trajectories parhta hai, woh isay pakar leta hai. Physician ko aisa alert milta hai jis mein trend, aage ka projected rasta, aur flag ke peeche ki clinical reasoning, sab nazar aata hai. Woh isay dekhti hai, ittefaq karti hai, aur mareez ke threshold paar karne se ek saal pehle nephrology ko refer kar deti hai.
Isi qism ki tabdeeli pakarne ke liye hum Chroneme bana rahe hain. Hum isay ek pre-registered protocol ke tehat validate kar rahe hain, aur jo kuch milega publish karenge, woh jagahen bhi jahan ye poora nahi utra.
Jo intelligence ICU mein ahista ahista hone wala drift pakarti hai, wahi primary care office mein bhi ahista drift pakar sakti hai. Jo system hafton mein upar jata heart rate flag karta hai, wahi saalon mein upar jata A1c bhi flag kar sakta hai. Architecture nahi badalta. Time scale badalta hai.
Pandrah billion dollars. Sattar clearances. Aur mareez ka tajurba wahi ka wahi.
Ye tab badlega jab infrastructure badlega. Jab clinical AI snapshots ke bajaye trajectories parhegi. Jab systems apni reasoning chhupane ke bajaye dikhayenge. Jab sehat aisi cheez banegi jise insaan waqt ke saath badalta dekh sake, na ke aisi jo baad mein ja kar pakri jaye.
Chroneme mein hum yahi bana rahe hain.
