March mein ek 63 saal ki aurat routine checkup ke liye apne doctor ke paas jaati hai. Blood test mein pata chalta hai ki unki blood sugar kaafi time se high chal rahi hai, aur unhe bataya jaata hai ki unhe type 2 diabetes hai. Wo iski ek common dawai shuru karti hain aur unhe teen mahine baad wapas aane ko kaha jaata hai.
June mein wo apna blood test office ke paas ki ek lab mein karwati hain, kyunki clinic tak drive karne se ye aasaan hai. Unki sugar March se zyada hai. Follow-up visit pe nurse unka weight leti hai aur dekhti hai ki lagbhag nau pound kam ho gaya hai, aur note mein likha jaata hai ki lifestyle changes ka achha asar ho raha hai. Wo aur unke doctor dono khush hain.
August mein wo ek urgent care clinic jaati hain, kamar ke beech mein ek dard ke saath jo kuch hafton se hai. Na wo giri hain, na unhone kuch bhaari uthaya hai. Unhe ek anti-inflammatory di jaati hai aur physical therapy ke liye refer kar diya jaata hai.
Teeno doctors ne apne saamne ka kaam kiya, aur har record akele padho toh theek hi lagta hai. Lekin saath padho, toh wo ek aisi cheez ki taraf ishara karte hain jo koi bhi akela nahi dikhata, aur jo doctor teeno dekhta, wo unke pancreas ka scan chahta.
Teeno visits ek saath kyun dekhni chahiye
Pancreas ek chhota sa organ hai jo pet ke peeche, gehraai mein, abdomen ki pichhli deewar se laga hota hai. Uska ek kaam insulin banana hai, wo hormone jo blood sugar ko control mein rakhta hai.
Yahi cheez teeno visits ko jodti hai. Pancreas mein tumor ho toh insulin banne mein gadbad ho sakti hai, aur blood sugar badh jaati hai, kabhi kabhi kisi bhi aur sign se pehle. Cancer se aisa weight loss bhi ho sakta hai jiski insaan ne koshish hi nahi ki. Aur kyunki pancreas spine aur uski nerves ke kareeb hota hai, wahan ka tumor kamar ke beech mein ek dull dard de sakta hai.
Naye diabetes wale zyadatar logon ko cancer nahi hota. Lekin jab diabetes pehli baar 50 ki umar ke baad aata hai, toh sau mein se lagbhag ek ko teen saal ke andar pancreatic cancer diagnose hota hai, jo us umar mein expected rate se lagbhag aath guna hai. Mayo Clinic ke researchers ne aise patients ko dhoondhne ke liye ek score banaya, jo teen cheezon pe based hai: diabetes kis umar mein shuru hui, weight kitna badla, aur blood sugar kitni badli. Sabse zyada wazan is baat ka hai ki weight gir raha ho aur sugar badh rahi ho.
Timing isliye matter karti hai kyunki pancreatic cancer aam taur pe der se pakda jaata hai. Lagbhag aadhe cases tab diagnose hote hain jab cancer phail chuka hota hai, aur tab treatment bahut kam kar paata hai. Mahino pehle pata chal jaaye toh bahut kuch badal sakta hai.
Is aurat ke case mein zaroori jaankari pehle se record mein thi. Bas wo teen jagah bikhri hui thi, aur koi bhi sab kuch ek saath nahi dekh raha tha.
Yahi gap ulti disha mein bhi kaam karta hai. Maine aise patients dekhe hain jinki cardiac evaluations hoti rahin jab asal wajah anxiety thi aur irregular heartbeat uska symptom tha, aur aise patients ka maine khud ilaaj kiya hai. Unke dil ko poori care se check kiya gaya, lekin jawaab unki kahani mein kahin aur tha. Jab kisi ki health ke tukde alag-alag pade hon, toh medicine jo serious hai use miss kar deti hai, aur jo nahi hai uske peeche bhaagti hai.
Ek achha doctor pehle notice karta hai, phir kuch karne se pehle evidence check karta hai. Medicine ke liye AI ko bhi aise hi kaam karna chahiye.
Computers ab tak ise solve kyun nahi kar paaye
Computers insaanon se kahin zyada tez padhte hain, toh ye poochna banta hai ki wo aise cases pehle se kyun nahi pakad lete. Wajah ye hai ki medicine mein zyadatar AI do mein se ek tarah ka hota hai, aur har ek tasveer ka sirf ek hissa dekhta hai.
Pehli tarah ka AI examples se seekhta hai. Lakhon purane patients ka data do, toh wo seekh leta hai ki kaun se patterns aksar kisi bimari se pehle aate the. Jo chatbots aaj bahut log use karte hain, wo isi tarah bane hain, bahut bade paimaane ke text pe train hokar. Is tarah ka AI notice karne mein achha hai, lekin wo ye nahi dikha sakta ki kisi conclusion tak kaise pahuncha, aur galat hone pe bhi utna hi confident lagta hai jitna sahi hone pe. Ek doctor aise conclusion pe safely act nahi kar sakti jise check karne ka uske paas koi tareeka hi na ho.
Doosri tarah ka AI un rules ko follow karta hai jo logon ne likh kar rakhe hain, "agar ye, toh wo" ki shakal mein. Rule saaf batata hai ki wo kya check karta hai aur kyun. Lekin wo sirf wahi pakadta hai jo kisi ne pehle se socha tha, aur tabhi jab jaankari bilkul usi form mein aaye jis form ki rule ko ummeed hai. Wo seekhta nahi.
Dono approach adhoori hain, aur dono mein se koi bhi buri idea nahi hai.
Ek gehri problem un records mein hai jinse ye systems seekhte hain. Medical record ka bada hissa notes hota hai, aur har note ek insaan ka kisi visit ka hisaab hai, jaldi mein likha hua, aur is baat se bana hua ki us insaan ne patient ko kaise dekha. Baaki ka zyadatar hissa billing ke liye design kiya gaya tha, jahan diagnoses payment codes ki tarah store hote hain. Jo system sirf isi material se seekhta hai, wo iske blind spots bhi seekh leta hai. 2019 mein researchers ne paaya ki American health systems mein extra care ke liye patients chunne wala ek algorithm Black patients ko asliyat se zyada healthy maan raha tha. Usne pichhle kharche se seekha tha, aur unki care pe kam kharcha hua tha.
Learning aur logic ek saath
Achhe doctors dono tarah ki soch use karte hain. Saalon ki practice unhe sikhati hai ki kab kuch theek nahi lag raha, aur phir kuch karne se pehle wo us impression ko evidence se check karte hain. Neuro-symbolic AI bhi isi tarah bana hai. "Neuro" wala hissa seekhta aur notice karta hai, jaise experience karta hai. "Symbolic" wala hissa rules se reasoning karta hai, jaise ek doctor evidence se check karte waqt karta hai.
Chroneme mein rules published research aur medical guidelines se aate hain, aur wo ek lens ki tarah kaam karte hain. Har clinical sawaal ke liye wo tay karte hain ki kaun si jaankari matter karti hai aur uske source pe kitna bharosa karna hai, taaki scale pe naapa gaya weight kisi note ke ek shabd se zyada maayne rakhe. Is frame ke andar learning wala hissa phir bhi aise patterns notice kar sakta hai jinhe kisi ne likhne ka socha hi nahi. Phir logic wala hissa har pattern ko evidence pe test karta hai, aur jo pattern tikta nahi, wo doctor tak recommendation ban kar kabhi nahi pahunchta.
Ab sochiye wahi aurat, aur Chroneme unki care padh raha hai. Unke lab results, weights aur notes ek record mein aa jaate hain, har us jagah se jahan unhe dekha gaya, usi order mein jismein cheezein hui. June mein jab unki sugar badhti hai aur weight girta hai, toh pattern wahi hai jo published score dhoondhta hai, aur unke doctor ko usi hafte finding dikh jaati hai, kamar dard shuru hone se do mahine pehle. Finding saadi bhasha mein likhi hoti hai aur uske peeche ka har result dikhati hai, kaun sa kahan aur kab record hua, aur wo study bhi jisse rule liya gaya hai. Scan ka order pehle se taiyaar hai, saath mein patient ke liye ek message jo unhe dara diye bina wajah samjhata hai. Unke doctor ise review karte hain, agree ya disagree karte hain, aur sign karte hain.
Kyunki rules likhe hue hain, wahi facts har patient ke liye wahi conclusion dete hain, chahe on call koi bhi ho. Jab evidence badalta hai, toh rule ek baar update hota hai aur sab pe laagu ho jaata hai. Jab kuch galat hota hai, toh pata lagaya ja sakta hai ki galti kahan hui.
Hum kya bana rahe hain
Sirf ek behtar method kaafi nahi hai. Use chalne ke liye ek system chahiye, jo har insaan ka record poora rakhe aur das lakhve patient ke liye bhi utna hi achha kaam kare jitna pehle ke liye. Wahi system hum bana rahe hain.
Isse naye tarah ki insights bhi mumkin hoti hain. Scan dikhata hai ki body andar se kaisi dikhti hai, history dikhati hai ki wo wahan tak kaise pahunchi, aur genetics dikhati hai ki uske saath kya hone ki zyada sambhavna thi. Inhe aam taur pe alag-alag padha gaya hai, alag-alag specialists ne. Saath mein aur sahi order mein padho, toh aise combinations saamne aate hain jo kisi ek record mein nahi hote.
Medicine dashakon se jaanti hai ki achhi, evidence-based care kaisi dikhti hai. Kami rahi hai us time ki jismein wo saara evidence har patient pe, har din laagu kiya ja sake, kyunki koi bhi doctor har visit se pehle har record ko har guideline se nahi mila sakta. Chroneme ke agents, yaani aise programs jo kisi kaam ke steps poore karte hain, wahi taiyaari karte hain. Wo orders, messages aur notes draft karte hain, har ek apne sources ke saath, taaki ek clinician review karke sign kare. Faisla insaanon ke paas rehta hai, aur bacha hua time wapas care mein jaata hai.
Hum apne claims ko lekar savdhaan hain. Har conclusion ko trace aur check kiya ja sakta hai, ye aaj design se hi sach hai. System itni baar sahi hota hai ya nahi ki patients ki madad kare, ye evidence ka sawaal hai. Hum ise ek aise protocol ke tahat test kar rahe hain jo koi bhi result dekhne se pehle likha gaya tha, aur jo bhi milega wo publish karenge, ye bhi ki kahan ye kam padta hai.
Sources: 50 ke baad new-onset diabetes aur pancreatic cancer ka link Chari et al., Gastroenterology, 2005 se hai. ENDPAC score Sharma et al., Mayo Clinic, Gastroenterology, 2018 ka hai. Diagnosis ke waqt stage ka data National Cancer Institute, SEER Cancer Stat Facts: Pancreatic Cancer se hai (51% ka diagnosis distant spread ke baad, 2016 se 2022). Care-management algorithm Obermeyer et al., Science, 2019 ka hai. Neuro-symbolic AI ko Garcez aur Lamb ne Artificial Intelligence Review, 2023 mein describe kiya hai.
Naye essays aur updates, email pe.